From 5a85ca0eeea0c766e62a9edccb07ebdd6fd7b04a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Matti=20Hellstr=C3=B6m?= Date: Thu, 21 Nov 2024 16:18:14 +0100 Subject: [PATCH 01/10] attempt at packmol_around to mimic old GUI behavior. This one also packs tightly in non-orthorhombic cells, but may fail for very skewed cells. SO-- --- interfaces/molecule/packmol.py | 141 ++++++++++++++++++++++++++++++++- 1 file changed, 140 insertions(+), 1 deletion(-) diff --git a/interfaces/molecule/packmol.py b/interfaces/molecule/packmol.py index 86c89c0f..9d15e577 100644 --- a/interfaces/molecule/packmol.py +++ b/interfaces/molecule/packmol.py @@ -1,7 +1,7 @@ import os import subprocess import tempfile -from typing import Any, Dict, List, Literal, Optional, Tuple, Union, overload, Sequence +from typing import Any, Dict, List, Literal, Optional, Tuple, Union, overload, Sequence, TYPE_CHECKING import numpy as np from scm.plams.core.errors import MoleculeError @@ -11,6 +11,19 @@ from scm.plams.tools.periodic_table import PeriodicTable from scm.plams.tools.units import Units from scm.plams.interfaces.molecule.rdkit import readpdb, writepdb +from scm.plams.core.functions import requires_optional_package, delete_job +from scm.plams.core.settings import Settings + +if TYPE_CHECKING: + try: + from scm.libbase import ( + UnifiedChemicalSystem as ChemicalSystem, + UnifiedElement as Element, + UnifiedElements as Elements, + UnifiedLattice as Lattice, + ) + except ImportError: + pass __all__ = [ "packmol", @@ -299,6 +312,11 @@ def run(self): return output_molecule +def sum_of_atomic_volumes(molecule: Molecule) -> float: + """Returns the sum of atomic volumes (calculated using vdW radii) in angstrom^3.""" + return (4 / 3) * 3.14159 * sum(at.radius**3 for at in molecule) + + def guess_density(molecules: Sequence[Molecule], coeffs: Sequence[Union[int, float]]) -> float: """Guess a density for a liquid of the given molecules and stoichiometric coefficients. @@ -748,6 +766,127 @@ def packmol_in_void( return ret +@requires_optional_package("scm.libbase") +def packmol_around( + current: Union[Molecule, "ChemicalSystem"], + molecules: Union[Molecule, List[Molecule]], + return_details: bool = False, + **kwargs, +) -> Molecule: + """Pack around the current molecule. + + ``current``: Molecule + Must have a 3D lattice + + The ``current`` molecule will be mapped to [0..1]. The "box" will be set to the min/max of the components of each lattice vector. + """ + from scm.libbase import ( + UnifiedChemicalSystem as ChemicalSystem, + UnifiedLattice as Lattice, + ) + from scm.utils.conversions import plams_molecule_to_chemsys, chemsys_to_plams_molecule + + if isinstance(current, Molecule): + original_ucs = plams_molecule_to_chemsys(current) + else: + original_ucs = current.copy() + assert isinstance(current, ChemicalSystem) + original_ucs.map_atoms(0) + + # step 1: find min/max of lattice + if current.lattice.num_vectors != 3: + raise ValueError(f"Input molecule `current` must have 3D lattice, got: {current.lattice}") + original_frac_coords = original_ucs.get_fractional_coordinates() + + original_volume = original_ucs.lattice.get_volume() + original_lattice = original_ucs.lattice.copy() + + # step 2, get remaining volume + current_atomic_volume = ( + (4 / 3) * 3.14159 * np.sum(np.fromiter((at.element.radius for at in original_ucs), dtype=np.float32)) + ) + remaining_volume = original_volume - current_atomic_volume + # temporary value to call the original packmol with + box_bounds_for_remaining_volume = [ + 0.0, + 0.0, + 0.0, + remaining_volume ** (1 / 3.0), + remaining_volume ** (1 / 3.0), + remaining_volume ** (1 / 3.0), + ] + # it is unnecessary to actually pack the molecules, this is just used to get the "details" + _, details = packmol(molecules=molecules, return_details=True, box_bounds=box_bounds_for_remaining_volume, **kwargs) + + maxcomponents = np.max(current.lattice.vectors, axis=0) - np.min(current.lattice.vectors, axis=0) + box_bounds = [0.0, 0.0, 0.0] + list(maxcomponents) + + will_run_uff_md = any(not np.isclose(x, 90.0) for x in original_lattice.get_angles("degree")) + + # we now know how many molecules to pack around the current molecule + + distorted = original_ucs.copy() + distorted.lattice.vectors = np.diag(maxcomponents) + distorted.set_fractional_coordinates(original_frac_coords) + # remove bonds to be able to do "shake all bonds * *" for the remaining molecules + if will_run_uff_md: + distorted.bonds.clear_bonds() + distorted_with_molecules = [chemsys_to_plams_molecule(distorted)] + tolist(molecules) + n_molecules = [1] + details["n_molecules"] + # in general we need higher tolerance here since we may be expanding the original system, + # and we do not want the added molecules to enter in artificial "voids" + tolerance = kwargs.get("tolerance", 2.2) + my_packed, details = packmol( + molecules=distorted_with_molecules, + n_molecules=n_molecules, + fix_first=True, + box_bounds=box_bounds, + return_details=True, + tolerance=tolerance, + ) + if will_run_uff_md: + nsteps = 500 + + s = Settings() + s.input.ForceField.Type = "UFF" + s.input.ams.Task = "MolecularDynamics" + s.input.ams.Constraints.AtomList = " ".join(str(x + 1) for x in range(len(current))) + s.input.ams.MolecularDynamics.NSteps = nsteps + s.input.ams.MolecularDynamics.TimeStep = 0.5 + s.input.ams.MolecularDynamics.InitialVelocities.Temperature = 10 + s.input.ams.MolecularDynamics.Shake.All = "bonds * *" + + l = original_lattice.vectors + target_lattice_str = f""" + {l[0][0]} {l[0][1]} {l[0][2]} + {l[1][0]} {l[1][1]} {l[1][2]} + {l[2][0]} {l[2][1]} {l[2][2]} + """ + + s.input.ams.MolecularDynamics.Deformation.StartStep = 1 + s.input.ams.MolecularDynamics.Deformation.TargetLattice._1 = target_lattice_str + + job = AMSJob(settings=s, molecule=my_packed, name="shakemd") + job.run() + my_packed = job.results.get_main_molecule() + delete_job(job) + + my_packed_ucs = plams_molecule_to_chemsys(my_packed) + my_packed_ucs.remove_atoms(range(len(original_ucs))) + my_packed_ucs.map_atoms_continuous() # so that we can add_other without having incompatible lattices + my_packed_ucs.lattice = Lattice() + + out_ucs = original_ucs.copy() + out_ucs.add_other(my_packed_ucs) + out_ucs.map_atoms(0) + + out_mol = chemsys_to_plams_molecule(out_ucs) + + if return_details: + return out_mol, details + return out_mol + + def packmol_on_slab( slab: Molecule, molecules: Union[List[Molecule], Molecule], From beb0993a8efe21102834f1071495fe078be499ef Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Matti=20Hellstr=C3=B6m?= Date: Tue, 3 Dec 2024 16:10:55 +0100 Subject: [PATCH 02/10] add packmol_around structure to packmol interface SO103 --- .../examples/PackMolExample/PackMol.ipynb | 322 ++++++++++-------- .../PackMolExample/PackMol.rst.include | 247 +++++++------- .../PackMol_files/PackMol_10_1.png | Bin 9407 -> 11162 bytes .../PackMol_files/PackMol_11_1.png | Bin 23968 -> 14835 bytes .../PackMol_files/PackMol_13_1.png | Bin 0 -> 4848 bytes .../PackMol_files/PackMol_23_1.png | Bin 25828 -> 15915 bytes .../PackMol_files/PackMol_33_1.png | Bin 18710 -> 18300 bytes .../PackMol_files/PackMol_34_1.png | Bin 20744 -> 21101 bytes .../PackMol_files/PackMol_35_1.png | Bin 0 -> 14313 bytes .../PackMol_files/PackMol_37_0.png | Bin 0 -> 20200 bytes .../PackMol_files/PackMol_38_1.png | Bin 0 -> 21327 bytes .../PackMol_files/PackMol_5_0.png | Bin 6074 -> 3982 bytes examples/PackMol.py | 159 ++++----- interfaces/molecule/packmol.py | 217 +++++++++--- 14 files changed, 546 insertions(+), 399 deletions(-) create mode 100644 doc/source/examples/PackMolExample/PackMol_files/PackMol_13_1.png create mode 100644 doc/source/examples/PackMolExample/PackMol_files/PackMol_35_1.png create mode 100644 doc/source/examples/PackMolExample/PackMol_files/PackMol_37_0.png create mode 100644 doc/source/examples/PackMolExample/PackMol_files/PackMol_38_1.png diff --git a/doc/source/examples/PackMolExample/PackMol.ipynb b/doc/source/examples/PackMolExample/PackMol.ipynb index 3c482631..97634737 100644 --- a/doc/source/examples/PackMolExample/PackMol.ipynb +++ b/doc/source/examples/PackMolExample/PackMol.ipynb @@ -15,7 +15,8 @@ "metadata": {}, "outputs": [], "source": [ - "from scm.plams import *\n", + "from scm.plams import plot_molecule, from_smiles, Molecule\n", + "from scm.plams.interfaces.molecule.packmol import packmol, packmol_around\n", "from ase.visualize.plot import plot_atoms\n", "from ase.build import fcc111, bulk\n", "import matplotlib.pyplot as plt" @@ -47,14 +48,7 @@ " s += f\", formula = {mol.get_formula()}\"\n", " if details:\n", " s += f'\\n#added molecules per species: {details[\"n_molecules\"]}, mole fractions: {details[\"mole_fractions\"]}'\n", - " print(s)\n", - "\n", - "\n", - "def show(mol, figsize=None, **kwargs):\n", - " \"\"\"Show a molecule in a Jupyter notebook\"\"\"\n", - " plt.figure(figsize=figsize or (2, 2))\n", - " plt.axis(\"off\")\n", - " plot_atoms(toASE(mol), **kwargs)" + " print(s)" ] }, { @@ -74,18 +68,20 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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PecmSJdny8nKzrKzsPQATeqshb7NliWhyPB5flE6n55SVlXk1NTWiqqoqGolE5Ewm4x08eNB+7733qL29XdE07Q8dHR3LALzN+RLQDzkbs0wm82Nd1zFp0iR3/PjxkUgkItm2LQ4cOJDes2ePkslkPEVR/jOZTD7DOfYgXvTa+Y579xcVx6JrJMi30NVJ4gA4hq51XwJfDaLY6O5qHo2uhY1H4q8x+xxdMTuS7wQJfH78IMEQ+IoYgwTDoPElyqDxJcqg8SXKoPElyqDxJcqg8SXK/wGgabDPqR1aNgAAAABJRU5ErkJggg==\n", "text/plain": [ - "
" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ "water = from_smiles(\"O\")\n", - "show(water)" + "plot_molecule(water);" ] }, { @@ -104,7 +100,7 @@ }, { "data": { - "image/png": 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", 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\n", 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" ] @@ -120,7 +116,7 @@ "out = packmol(water, n_atoms=194, density=1.0)\n", "printsummary(out)\n", "out.write(\"water-1.xyz\")\n", - "show(out)" + "plot_molecule(out);" ] }, { @@ -139,7 +135,7 @@ }, { "data": { - "image/png": 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", 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\n", 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" ] @@ -155,7 +151,7 @@ "out = packmol(water, density=1.0, box_bounds=[0.0, 0.0, 0.0, 8.0, 12.0, 14.0])\n", "printsummary(out)\n", "out.write(\"water-2.xyz\")\n", - "show(out)" + "plot_molecule(out);" ] }, { @@ -174,7 +170,7 @@ }, { "data": { - "image/png": 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", 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\n", 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" ] @@ -190,7 +186,7 @@ "out = packmol(water, n_molecules=64, density=1.0)\n", "printsummary(out)\n", "out.write(\"water-3.xyz\")\n", - "show(out)" + "plot_molecule(out);" ] }, { @@ -209,7 +205,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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\n", 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" ] @@ -225,7 +221,7 @@ "out = packmol(water, n_molecules=64, box_bounds=[0.0, 0.0, 0.0, 12.0, 13.0, 14.0])\n", "printsummary(out)\n", "out.write(\"water-4.xyz\")\n", - "show(out)" + "plot_molecule(out);" ] }, { @@ -238,26 +234,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "water-5.xyz: pure liquid in non-orthorhombic box (requires AMS2022 or later)\n", - "PLAMS working folder: /home/robert/workspace/ams/main/scripting/scm/plams/doc/source/examples/PackMolExample/plams_workdir\n", - "Top: system in surrounding orthorhombic box before calling refine_lattice(). Bottom: System in non-orthorhombic box after calling refine_lattice()\n" + "water-5.xyz: pure liquid in non-orthorhombic box (requires AMS2025 or later)\n", + "PLAMS working folder: /home/hellstrom/temp/temp-Lgc-2024-Dec-03/plams_workdir.006\n" ] }, { "data": { - "image/png": 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", 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", 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\n", 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" ] @@ -269,36 +252,24 @@ } ], "source": [ - "print(\"water-5.xyz: pure liquid in non-orthorhombic box (requires AMS2022 or later)\")\n", - "# first place the molecules in a cuboid surrounding the desired lattice\n", - "# then gradually change into the desired lattice using refine_lattice()\n", - "# note that the molecules may become distorted by this procedure\n", - "lattice = [[10.0, 2.0, -1.0], [-5.0, 8.0, 0.0], [0.0, -2.0, 11.0]]\n", - "temp_out = packmol(\n", - " water,\n", - " n_molecules=32,\n", - " box_bounds=[\n", - " 0,\n", - " 0,\n", - " 0,\n", - " max(lattice[i][0] for i in range(3)) - min(lattice[i][0] for i in range(3)),\n", - " max(lattice[i][1] for i in range(3)) - min(lattice[i][1] for i in range(3)),\n", - " max(lattice[i][2] for i in range(3)) - min(lattice[i][2] for i in range(3)),\n", - " ],\n", - ")\n", - "out = refine_lattice(temp_out, lattice=lattice)\n", - "if out is not None:\n", - " out.write(\"water-5.xyz\")\n", - " print(\n", - " \"Top: system in surrounding orthorhombic box before calling refine_lattice(). Bottom: System in non-orthorhombic box after calling refine_lattice()\"\n", - " )\n", - " show(temp_out)\n", - " show(out)" + "print(\"water-5.xyz: pure liquid in non-orthorhombic box (requires AMS2025 or later)\")\n", + "# Non-orthorhombic boxes use UFF MD simulations behind the scenes\n", + "# You can pack inside any lattice using the packmol_around function\n", + "from scm.plams import init, Settings\n", + "\n", + "s = Settings()\n", + "s.log.stdout = 0\n", + "init(config_settings=s)\n", + "box = Molecule()\n", + "box.lattice = [[10.0, 2.0, -1.0], [-5.0, 8.0, 0.0], [0.0, -2.0, 11.0]]\n", + "out = packmol_around(box, molecules=[water], n_molecules=[32])\n", + "out.write(\"water-5.xyz\")\n", + "plot_molecule(out);" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "id": "de56451a-b304-4003-ad75-1df927d2795b", "metadata": {}, "outputs": [ @@ -313,12 +284,14 @@ }, { "data": { - "image/png": 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", + "image/png": 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VxR49etQIIcYB6BkMBsPr16+vk+edd96ZCgQC/9zedvGAl+/QtFShQK+zbRpABEBwR/DYaWDn9h4AQz0gvKSAKfSfpskM4D0a6DRnlgsRyqdp+tyy2KmigkuWLGHnzp2Zj2677Tbquv6QBFzkAatUIOEBYQVIekJsMQwjdu6558YvvvjiRDAYDDuO8zQyW4aWZd1UXl4euuOOO1IvvPACx4wZE7Isa0Oho6qBtuppA6HPC2ibmOOwnyzT1bRKWZYnAVC2o7ydO7Kz+QIA4QHf/72Ibb97dd0PAHPy5NvNAULZnWaVbXOJZTHhOJxhGDx20CCuW7eOJSUljEaj9cA+77zzYrIsX11bNwAOgBbI2GQD6AbgzwCuzNfhABzsuu4zZWVlc2VZngygZEdHkQSMcoHQrKyZJjessW0eapo8evBgzps3jwcddFDI87wZ2wE2C4q3Pb0HwIEVQtTk2nA1Fqoch1Z6qmqdna8DTLtS0xJ0XX5lWTzY89jMddm5ooJtbJu3GAZbBoOMxWI84ogjOGXKlDpAr1ixgrZthwF02FGAdnYAcJgDrG4ORE5WFP8lw+CXlsWZpsk/yLIf0HVeecklTCbTYkMkEmHz5s1rABxQZDk7fWRvy9AFnrylCJ5UG85W1aiUdRIEgGMA4Z9sm+sy1i2PP/ooE4kESfLzzz9nx4oK9jJNXjBuHJcsWcIOHTpw1KhRnDZtGi+99FI6jhPWdf2C/zKo/RzHebykpOR1IcR5AJzM82GGYVT169cvNOa445IBz/M9y4oEgOWe68ZqrXOy6fLLL/dlIf5tA1Ml4LpChNvfdBovE+Jfr+cxr2kq3KfrdIBpWXkO3EeSttJ1eaNpcsIZZ9T7+Llz57Kn5/EYz2Nzz+OQAw5gwLLoeR4Nw3gYQLf/JtC2bd9RXl4euu2221JPP/00jzzyyBrbttcA6GOaZujDDz/c9i2RSITDDj+cJZoWLy8rq6caJcmJF1/MI2SZ9+o6L1XVZCkQCgILARxdCDY7C+xtvadMiA/ymcQ2Fe5Pg/1YVp7DB2ZMkI8tKeGMGTPqfXwqlaIkBJOOw2W2zVmmycWWRZE+o9zk9t9vGQAcWF5eHtqwYUOdOl955ZUJy7KWnnPOOfUEjJUrV7LEMNjdcfj666/Xebd582ZWeB4XZ8kvCcfhq4bBZkKETODKprBpLBSsIWLWFlqCXPm97xdt9fCd78cjwMqsR6HqdGXRPJnED8uW1UuzcuVKlOg6JABdJAl/UBS0lyTIQJJkcbrSnUy2bZ81ceJEo7y8vM7zyy+/XJEkqUvfvn313DTt27eHrCi4OZnEuLFjce211+Kjjz7Cc889h0N698bpvo+9svTqihAYrar40rKsoBB/UYU4KzdPFrr1vD0jG8Ch7YWoLnQLj67LkOPQTgtoHbPyKTeAyCbH4QLLYtuyMq5du3ZbT08kEhx08MHcW5ZZJgRVgKUAD5AkWsAP+C+P7GAw+Mpjjz2WO3jp+z51WfZPHTu23rulS5eymWky7ji8UFW5j2XxgJISDg8G+ZphNLot+pVl0QSqAOjbM7K3F2zhAav+UcRU/piuM5inUh7w6l26nqLr8nbbZrnjcOL55/Piiy5iUFHYWZL4sK5zXWYPeb1t82nD4F6SFLHT57X6/LfABnD2oEGDqnMB/fvf/04d8MtNk2+++ea251u2bOHggw7i9badtioVgl8XuRV6sCRVAzj5NwU7N0jAyDIhwisLsKX+wrLoACEAffM02EFlQoR+zeTzvW3zj6rKIMBput5oT/+bYdQqaw7axSA7ALoDONA0zdUTJ06Mb9y4kalUinPnzqVtWbHhshyZb1nsYNvs3aULjx4wgEHT5IUZPjzLNNk/x4y5kPCKYbAE+GS76l3EB9brPTowqUyI0FzTZL41d8Jx+IJh0AHCEjC6obwt4PbuklTzS+bwXCch+D8FKmxmmSbNtHOfwC4AuZcL/I8BhFsJUdVRiEoPiAQkqVqW5YSqqnHP85YHgI9qbduSjsP3TZOvGUYd2/WH0vsHRYP9nW3TA9Ztz8j+j4F301RvgzxK3icLseKESOReF2h+hKJYcUAqEQI2kHoskYj5wLIa4CKSHzaUcRi4cpXvp7qFQpOGyrLRTpKk07LsuxujPygKjlAU5c1k8gwADxTxPXlJCKEBKAewmWQ080yygftLgbMnaZp+jqrKtXbkPol3UincFY/XzE8kolWJxMgy4H43k58sBA5V6jezj+2wvMikYf2t6V1jvJAi36gC+lc6zuYPO3ZMlV9wAb85+ODUfZLkbwYu3EL2agxoIL1IDJFXbwX6/yOV2nphgUDX0iWqatvAZSLHzEMIYQghTisR4oUyId4qFeIVWYiLhBBebh5CCMW27VsNw9jked5yXdc3u677iBDCcIBp3SXp7OWOY12j63L2gYHaw4JvW5bzqGGUmcD8OJBs7GAWALQQAisKODuWSz/4PhRgfc7jXWe8EAgEXrzwwgtbffXVV6rtOOKbxYtl3/dVx3GelGV5ohDiwIAQL5UI8ZMnxIYSIVY4QjwkhOiak9XyEFAyIs9IaIwGyDIUoBRp7wkQQmi2ELebwPpDZPmh23T9pEcM44i7DGPM0bJ8uwH84gnxRDborus+0qtXr4nffPONU1lZaf7www/mYYcddobruh+WCjH2HcuySpqwHT9FVcU9uu4Q6Dk9Hq9pLO5RioJPUin8VCTgD8XjoUpgWlGJaqkIfsUGnrd1HCcSiUQ4atQojhgxgsuWLaPv+5w+fTpLVNWvECJ+h6alFloWl9s2P7UsXqaqcReIBNLWm80yeY0NClE0H6PrspMQlQD2A2C6wIdDZTn0fQPC4xrb5umKEnWAZQCaAWhhmmZ0y5YtzKZYLMaSYDB1WyNWqLkh5ThsKUSNBkR+bEJ4naiqvKKIvH9Ob4WGAXiFYJMbdoYNWus2bdrEvvnmG3zxxRd4+eWX0aVLFyxatAiTJkzA7Yoi1tq2ermuS/vKMjpLEvaXZdxpGOoGxzHOVdV+NvClEKLCdd2xqSIN9Wopmt7tinjAi4cryn5zTdPaowF78laShKcNQz9fVdu5wLsADu3Tp088GAzWiadpGo444gjJLfA0CJCe1v+kqqYGVPeXZXYVAqcD+CqPrdwkTcOTiQTeSjZ5tA1hEsMjkZAM3EuyKuf1zp3G2bCW5vuVK1fqs2bNwogRI6BpGiorK3HM0KF4MJnEuYoCqYHG0oXAnYah/UnTmrvAPxRFcUzDQLFGhD/7PjaTCoCgJsTQ6YZh5jtnlk1CCFygqprtOHspivL8zz//7GZGSR1a+9NPaF4E2ABwjKJIqqqWP/nyy+L1jz5Cj6uvxlAh8EEOqB0kCdMNA8dHIpgajyOeKT9FYpnv4+fMFL8klcJB4XDoB99/PZQ+VVOHGsGmDu2w5wWSmzVNm/7SSy/FN2zYAAB44P770SIUwmbfx6oCeNKNmqaWCdGxsrJyWfM2bRKPFHHCAwAeSSSSCvCcC5w/UVV1vQBwtpI4TAhcMHmyvGnTJllRFLz22mt14syfPx9fLlyIo4uUITwhoJmmGDZsGLp3747L/vxnPPLMM5iYJXiu933cGoulxkaj4SQw74pY7PPmNTWRYyORVDsSg00TPYVABen3CYerl/v+TdXAqczjSKdQzws7tM7Oeqc7jvOCYRicPHkyLdPkiSNG8Izjj2epbfPyAk5HPJg2bnjbMIyNjqJweYEH39el151hAPuoQDSfS6plts0pus7bNI0LMxqrew2DJx177Db+/PHHH7N58+Y85ZRTOHXqVJ511lkJ0zRjfSUpVKz8sNiyuEfLlnX4fzKZpG3bDLpuKmBZSQWIecALyNq7BnB+eVlZYsGCBSTT6uLHHnvMN02zEkCr7cGmTrxCwS5IAJCkR1q0aME1a9Zs+8hNmzax1x57NKkkqXQcykASQEdL15e0FoJNaed+tW12k6QaE7gZQKkJRHPjXOM4LHccXjBuHCddeCFbl5bybNflccEgX3jhhTqAbNy4kVOmTKFt29x3330ThmGstYFIqEjjwZsMg+NOPrke2IFAgM8//zzvv/9+v1mzZiHLsq7Kbr9gMLg4dyeMJM8999yIrus37yg+O2Vk14aSkpJ3n3rqqXqVnTVrFg8KBJpsJAOIIWN0pwIPBYTgbabJDTmgZ05A+uVChCzgdqSFs1ITqHOi8w3T5J5t2jB7C7Kmpob9e/dmf8Pg3TlWLyS5bt06Oo7DVCrFk08+OeoJ8eMTRZhfJR2H5ZLE2tFZS3/729+4//77b/u/Zs0aWpYVQcbCJj0bCz/X+xNJvvnmmywrK1uwI9gUCzabihMMBld8/vnn9Sq7evVqtipgWlbSI7vWw+AfR44cGT3rpJMYNAwOKSnhcSUlPKKkhJYk0QUWIOv8EwBZA6Jrs8o5JhDgM888U68+7733Hrt5HjtWVHDTpk3bnvu+zwsvvJDnnnsuSXL+/Pm0bXtthRA16wtkK3doWtKTpK19+vSpmTNnDhcvXsxzzjmHjmFwxOGH848TJvDRRx9ldXU1x48fHwVwae03mKZZ+d1339Wr7/333+8HAoFXdgSbYsFusvcEAoG/33ffffXML6ZPn86hTYzsRZZFG9iUVd5JAwcOrCLTrGDOnDl86aWXOHPmTDZr1iwEoFdu+S7w1PUZeza6LvcvKeFHH31Ur/HWrl3L5qbJybbNgG2z7z77sEeHDmxmGPRUlQfttRdPPPpo3nHHHQwEAj+YwB3NJSnWzfNYahgcEgjwrTw7fg/oetICNgLoAOBc2zR/9iQp1VaSeL2uc6qu8x5d50jPY4llcf8ePXwAt9TW37KsKSNGjIjU2qSRaX9oFRUVNQAG7Qg2RYFdYIc4MBAIhD777LNtlf3222/ZvlkzzmpiO/RsVY3owPVZeVmGYVS99957dYCaOnWq77ruUuQ5wgqgZykQqj2VOc51eetNN9UD+4knnmAXTWMZwEMliVN1nS8YBqfqOo+SZQYBHinL7KGqtIGwY9ufHXjggfEPP/yQ69ev5/Tp09m6pIQvmCYjts0bNY3thQibwFqktXjCAaZ1FqLmvQasS3+ybZ6dzv8XZE6eALBc113QuXPn6ptuusm/5JJLEp7nhW3bvnWn4FMEkAX1HiHECaZp1nTv1o0H9+jBMsPgtCaAXm3bNNOGDS1zyjzMMIya4447LnTbbbfxsMMOq87YdDdod+YCrxwty+G44/Ary2Iz2+bbb7+9zd7r/fffZ1CWOVKW+UMDU/OPts0TFIX9MnvpzcrLGQ6H63SYjz/+mLZl0bYstmzZMjV8+PBwixYtajzPW2QAd+8tSTVbCxDsbta0hAMsx3+MFCUAh+u6fo+iKDcC2GtnYbPTwc7E1QzgvV6SFNnYBK/7xbbZWYiahvyPI63zvkjX9XsAnIkG/HMD6BYIBF7TdT1kmmaynWEk5pkm3zZNdnEc7tGyJfdq25YlksQzFCXvlmx2SDkOJ6gqOwvByZdcUm92IMnWrVunzjvvvEQqlSKZtpe7+uqrE45t+z8XaJTgOw6PlOWQlN4Z3DarAWiOneyVeGdO4ZKiKBd6nveToigJz/OW28DnewlR83fDqOegNeQ4fNIw2DxtSHdLvmm5iLL3NE2z6vbbb0+tW7eOK1as4MUXX5yyLcvfV4jqezSNN2kax8oy2wvBRIFLqYTjsEIIXjhhQj2gfd9ns2bN+M0339R5nkwm2ap5c35WhAXKPNOkl/bm2NzzvBmapsVs2446jrNOUZSzdhpGRTRoo73HcZyH9tlnn5qnnnqKS5cu5axZs1heXh41gaogkGouBE9SFJ6rqhwpy74NxAPABwCO2NGP8DzvlVtvvTWVC8jYsWMjiqK85ACPlwCvecDq+zXNLxQEui6vV1UGXJe5FqRvvvkmmzVr5uczBx51xBFNOrXNHd1thKg2TXP1pEmT4ps3b6bv+1ywYAFbt24d0jRt/I5gsz1gs5F37UzTTJSXl3OvvfZiaWkpK8rLebBt882MU/kvLIuP6TqnaBovUBT2FSKU4VVFH4vNDaZpVq1evbpeo7/zzjssLS39KlNHRwNihfDR7FDlOHRUlXu0bs0nn3yS8+fP57XXXpuwLCtSXl4ezgU7lUqxY0VFUSObrsuhshzu0qVLJDe/Tz75hLZtr0cjxpWFLr12iucFVVXv7NChg/Lvf/8bixcvxrp163Dp5MlYS+IwWUYNgA9SKdyTSODKeBzPJpP4nLTaCdFJBxYKISqKqAeEEJIQop8Q4lghRBtJkpLhcH2XahmHs7WH9Zt5QiQCRW5quEKgIpnE5I0b8cSkSTjmD3+I3Hvvvc+Gw+H9YrHYqsyMAgDwfR/XXnstmoXD2K9I/b4AlEMOOcTIdbNxwAEHQFVVG0DrRpIX5hWjiJGdd6oAoFiWFcrlXSR57ODBnKAoLAF4oqLwn1nLkIjj8FnDYG9Jop3WbRd0sgNAL8dxfmrfvn31oYceWmlZVsR13aXjxo2ro3pKpVIcMGBAjRBiQiZdx3IhaooZbbWhkxD83rb5d8NgWdoNVW1d2rquu6RNmzbVI0eOrGrVqlWNa9uJt4qYwrPKiJ100kmJ3Dasrq6uPSve4GHDhrCpF68IsPNOFQDaBoPBUD2kSZ5x2mlsJkSTnoMe03VaaRfLjV5/BMAzTXPL888/v41XVlVVcciQIWHHcapGjx4dnj17NmfMmMH+/fvXuK77b2RsrAHY2zuNuwC3Og6v17Rk9vGlTL4CwEEATgHQVwNuOkdVI8WU8Una+naj4zjhn376qU4bXnfddYlAIPCP7cFmR8BuaGS7uq5HN2/eXKeS33//PQOKwm8K5F2P6rpvpyXSBpcbQojzjj766JrcTrVmzRoahhGWZfnq0tLSz0tLS/8thDgHWcb0AIIu8M19ul6UgPawrvM4RWHCcVguRF7NXU57tLKAyJYiOtWJihJWgSsMw7jU87zwlVdemZg2bRqHDh1abdv2WgBttweb7Qa7seB53qsXXHBBLFu4OGPsWE4uwlTWdxx2FaIKwJENlWNZ1sN33313LtYkyRYtWlQD2KOBxtjLNM3N/fv3j3QyjEZ9kGeHhONwb0niu6bJFwyDQeCrQtrDBR4ZIMuhaAHlPGkYvg38ioxLDwD7mKZ5byAQeAHAeAD2zsCoKLAb6z0AylzXXdy9e/fEDTfcwDPHjqUJNLlFmRseT58amddQOZIkTTrxxBPrqLISiQQ/++wzapoWBbAP8kitnuctnjp1qu/7PocPHswzHacgpcp4VeWRssyPTbP2kMPBBbaV4gJv9pWkmm8bmNmqMmzBAjYB6L5DIO5KDRoA1XGcx1VVTbVs1oympnFvSSpaSNniOFSBWGOdyjTN6nfeeYdr167lDddcw9YlJWyuKGwnSfFSIGQDG7Id4gLoWlpaGqrdXKiurubA/ffnyMztefnqsdy2eZyi8CBJ4p2a5ttpoI8pEgDJBK41gcqDJKnqMV3n66bJFw2D56hq1EobW87BDniU+s3Abix4nvfIYYcdFtq4cSPJ9FmnwUWuM2uncpG2n2/QrwiAQzVVDdmSxLN1fZvlSW343LJ4uqKEzTRAowAc2LVr1zqn3qPRKK+89FJ6msaDJSn2oK7zWcPgQ7rOwbLMAMCeQiSMNCCzAfTeASA0ACcFgRllwL9Kgbk6cCNyPFDsirDDvQdAUNf1yK+//rqtMefNm8cBJSVFgx1OW6skGquHAE4oAcK5IOeGzyyLgfSS7kTDMEIrV67MxpupVIpt27atBnCLBzxTAsyxgY8EMFMF7gUwCY2YAu1wwwODgsHg3GAw+GMwGJyLRrYwtxebHQGbDTzfv0uXLluzG3LDhg0MGgY3FbnMeS0tBH3dSB1amECoKaBrw6eWRQMImaZ5Z/fu3Wu++uorkuQvv/zCU045Jeq67qfYSXd0FhNUVZ1QVlYWmjp1qv/ll19y2rRpfllZWUhV1fORdnY4GmmngaehgItZd+XSq7Vt25HcLcBTR4/mnRkn7oWGgyUpAuC0huqgAdedpShFrWFPVJSQDFxuGMallmVtdhwnout6xPO8pwG4uxpoAJ5hGOHvv/++TnstW7aMuq5HLMta26dPn6orrrgiNWTIkGrTNLcCOHB7sNlusBsLgUDg3csuuyyevfR66KGHWCYEfy1QIp9nmrQkiY7jvJJvtAFQbGBTsa6dP0orLH5Bep9YBlCBRi52zShJ+iDtYXC7bskD0N4Ebi8DFpQCC0uB9yXgj0hfeDN6wIAB9T3nkDzkkENSZ5xxRir72RtvvEHTNDcj5wD+dtWriA9osPcAqHBd97uuXbtWTZ48OTFs2LBqwzCqdeD1bpKUaOrG2fmWxeaWxdmzZ7Ndu3bVyOMNEUD7EqBos17/Px4fmryuAsC+ruv+2KpVq+q+fftuNQwj4rruw/mWcw2kbxMA/mEDkYtUNTrTNPle5pqoEYpSYwBhG/jHoYcemhfsQYMG1bN4JckDDzywCo0ced6lS6/MewnAMABXIG1k4AAQFnBrCRC6SdOSuaAvsixOyJj6zpkzhyQ5ZcoU33Gcp/Pkv3dbISqLBZuuy+ZCVAPo0kT9g6Zpbnn22We3qWI3btzIgw46KGRZVpNmvAC6WMCG6zUtUdOArLLOtnmsLIcdIfylS5fWAXT16tU0TZO1K5psGj9+fARZxg2/Odg7EgDs5wLP6UCkixBb95Wkra2FYOuSEt5wzTV17MwffvhhBgKBF/Pk0S64/SM7CqB5Y3WUJOmPo0ePrqeKXb58OU3TrAagNfJ9jg2seSTjLqSxkHIcnqAoqRJd59/+9jeuWrWKr776Ktu0aRM3TTNRU1O3Cr7v1852A3YYhyIAK6j3NJFHAMD+AAbatr18+vTpdT4slUqxV69e1QDGZKXRkb4M/XQT2PRJkTz7A9OkC/zclNTted7TDz74YC7W/PXXXxkIBEJoRD8tgPOPlOWCd9RiGQuY/RyHrWyb+wcCPFiSoo5lJY899thE7WnScDjMiRMnxj3PW9hY/XfZ0isnjkDaT3iTyxkAgxzHCU2bNo3r16/nokWLOHLkyIjrup/hPz7GB5umuWW//farHDVqVLXruomutp1s7B7r3JBx7Typqfqoqnr1uHHjIrUgf/DBB+zbty+DwSBN02QgEFgG4CxkeXuq/WYP+PHdIpwJ0XV5g6bxvJy9g2WWxQrTTJiGkezevftW27YjgUDgnaYExV259FIBHB8EPpGAlAIkJSAVBL5CetuvQSkSadPjd3VdDzmOs94wjFuRUfwjfWa65t133902ysLhMIcOGcLTCpTwPzRNGmkHO8ECvq+VaZqhjz76iAsXLmSzZs348ssvM5lMMpFI8Nlnn6Wd1pFHAsBSAGcjbRi4d3MhaopxE0Y3bVEbAOo93+I4bJs+GTMbwLhCpPBdIqAB6GMB6/eXpKoXs27RiWYuIusvy1UmsKVYfgNAVlX1qjPPPHPbSKulVatW0TAM/quJkZSZvsMAhuXmDWAogHMAXADgOPzHjPdowzBq2rZtm8h1iEuSN157Lcc5DmebJg+T5Wo7zR5O3T/jkrNYWUIFmG9nLHOhu3+AEJUmUGUAdza2mvjNp3EA+5tAzatNWGW8ZZrMbCQMaiRvCcBRQWCeCsQEQNuy/GnTptVrcJL0PM/XAY5WVWY7ovcdh/8yTY5SlBoTqM4GGkBABa6ygQ3dJKnqVEUJnaUo4QGyXGkAYSftuqITAM9xnOSyZcvqlbtw4ULunXWy5WFdT+nA1l6SVPQqIZVWDee1dE05DrsIwfmWxe9su3bjZD0asCH/TadxAJYJbC7U3/i7afdVVchjWgOguw38tKckVT9hGNycOd57k2HwjDweAlesWEHXdblixQrec9dd7NyiBR1JYrkQCQuIusDqWgVGVhltHeDHUYoSySfgrbJtXq5pCROoFsB9zTzPnzdvXr2y33zzTQ4IBuukvURVEx6QKkaOoOvyG8ti60Zcilyjabw6ywXHk4bhZ44W1TPQ/E2XXgDOGlzkpSdj0mrLP+Xks5cJVD5hGH4uz/vVtllumpw9e/a2xq6urubAgQN5xRVXbHvm+z6XLl1KTdNiALrmCocASmxg1a2almyqju8YBl3Av1jTOKRfP8ZisW0nSSKRCA/u1YuP5aiAI47DAJAqxnSYrsuLVJXXNOJP5QFd5wU5AtyfNS3hAS9sD2ZkEdal2af7A8CfL9U0p9C0APAnTbMsYHLt9UlCCNUG3plqGO7Zqprr1QrNJQmvAZgwZgz6du+OY4cORUVFBbZs2cIbb7wRmbkLtd4eOnfuHEV625DZ+ZjANccpSosrdb1JZy2SEGgBiH5CYOknn8DRdUiSBFtV0b6kBOrSpTgjxwuDIQQGyLJ/SzyeSNUtukHa6Pt4PpHAuY24AIuQMHOeXaKqShwYKYQoyX5eqOeFol1jCSGCUaDTsCId3fSTJChAEEC7zKNju0uSc7qqNmjbe4iiYIUk4dZVq3Da/Pk4IhaLR0KhRDwex2OPPor9unZFt/bt8Yf998e6777zTOANU4jbhRDtMnU1AIz/i67Xv7M4D72cSGAjgEeTSdyjqtjoOEg5Dn4yDNxMojIWQ69IBEtzXIdMUlVlle8nzovFYn4TgFeRODYSwfmahjaNmBv/K5XCPjlt3FyScLSi+AI4tZDvyaWCnYXwP05aAjYQl4UoyjOdEAJBIZJbyCAABIE/T9Y0t/FUaRfMQzOjqZMkaYesWMFOLVuinyThjngcQxUl3WMdB9+mUs6DicSkpxOJiboQlwPYuJ8soyGvSdk0L5nEi8kkXjRNDMsZvWUAztE0jFdVPJVM4tBwGPMyF5wDQECSIIBfX0okKtf6/p636brZMweoWk+Il8ViOFiWcVOeO7Nr6Uffx0e+j5fz+HIZIMvW3GSyR/YzFuhAp2Cws67tDce2zxMjwumZJCSE0GSgz6giHdNI6SAeTSTSt9vnpO8uy3hIlvXJmoaB4fDtv5AfDpblJtnNBt/HidEoXjNNDGqkTkIInK2qkAEcE4ngW9uGKgS2kFCArZuAg99Jpa58Pxy+eE9JUo5VFMcRQqz3fUxPJhERAs11HQc24lSIJG6Ox3G6osDKc6BBB6AARk693i8E8O25eHUTgJovGnBfRRL5prIffB9V6ec/AQgYQFwr4nRGksRxkQieMYw00I1QR0nCh5Zl2cDgzb7fZCFPJhI4RlEaBTqbzlBVtJIkvJlxdfVSMhmtBmaSjMbI68JAxZe+f+ZN8fg918RiyQcBHDVuHN757DPc/sILeKRLF4wHkCNegCSujccxP5nEtXo9v/QAgJ9JPwKsy3l8aN7IOVT08R+SfgJ44P54PJL9cguJi0mUJBJQQyEcIkn4R5bfr7/G42S6QWJIzw5K7sc2RrOSSbQUAmMK9GvaXpJwiaZJ/2riXswUiUcSCVxQpL/UC1QVDyUSqCIxPZEQcWBq7TuSCZIzBLCuxHXlZ55/Ho899hh69+6NESNGYN4nn+Cfto2PMyOcJF5KJNA1lcI9sow1uo4/CoHlOTNAisSjiUQ0DrycU53fzuldHHj05WRSfJsZ3TESQwHERo/G18uWIRqLYdLjj+N0TcPsZBIrfR/PJBJCAUaq6Zvywgaw4eMi/HY+nEjggkb4XD46V1WlZaT0ayPlLPV9yAD6FClwjlYUfJBK4dJoNKal7ytbm/1eCCF04LIYKUaPHl0nrWVZOHXCBJyVSGBEJILONTU4R5Jw7LnnYvGSJfhu+XLscemlGEDWcWY7J5VCglxF8vPs/Arl2dulVCHTt+w1FyK01Lb5nGHwsAMOqHebzaxZs7iP53EPgA/oOpfaNkuBkASMloFLT1CUgrcsLYCVRSou6LrsKUnJqxo5rDDfsnjgdpg903UZSF+6/iPy+DoHUKoDseaex9rD+tl04w03cIRhcIZhcLxh8Jw8Nx9de9VVPCdT1lrbZkX6RMrIprDZ4XV2LiXJpzeTE/uEQol7FAUnjhtX76LvI488EstjMZyuabhY09BVkjDTsiwDeDwFPPtGMim+KcB1pU8iDKBJ0T0PBYWIPpBIxBrytGghvVNSLDFTpzAwkGQ+j9O2ASRbAJg9e3adF7FYDM8/9hguEQKjVBWLDAOnjx9fL4PTzz4bc3wf36RSOCAcDleTt5P8+3ZUF8AO+i5NAk/6QOWKVAqbNm2qlyYcDoO+jz9lTb/9ZBndJEkBMCABnHNYJBL+rgnAN5BQkVZ2F0tbSQW2vbFXKpV8MZGo976zJGGl7+OXIl1Bf+L70NO2bT/nvstoiJwwoE6Jx3H2SSfhwQcewIoVK/Dee+/hqEMPRc/KSgzIsA4N6bbKpVAohJAQ7BsOV/9KTgyRN+Wry28+jWeeDe4qRNWNmsaKigpu3bq1zjR000038WjPqzf9PZ++5+LDDDu43gFiV6lqcqVl8U3T5FW6zrt0nV9bFqfoeqoMCLnAmgtVlU8bBn8qcIvzF9umJUl86qmneMMNN6Q8z2NnWY68Yhj83rb5o23zHcOgCfDGIo0iTlSUkAJcVm+qlKQxruuu8TwvbBgG+3oeXzEMHu95bOc47BMI8K+GUWcD5D5d5/DBg+tN96edeqqv6/osNLHNmQ+bvPGKAJt5np17mqKEfMfhHyyLbVq35oMPPsgZM2bwxOOPZ2vL4oo8wHxn23SBXzzPm9+sWbOao446KtQ8EIg5lsW9unXjDTfcwNNPO42mYdCUpI9lWb5F1/XY4EGDeNLw4SyxLE4uwB/qjbrONmVl7NixIxctWsRly5bRMIx4APjCBX51gI0BYKkkSX5Ly2JThpG1YXHaHj0MoCynPYaXlpaG3n//ffq+z0gkwnvuvputLIuNOc0LOw4PcV0eOXAgX3/9dc6ePZvDhw+nbdu+67oz0YSxZD5sdhTsfCP7wvGqGqab3l6cYxg8znV5ZEkJ7zAMNnRh+A+2Tc+yYuPGjYvWnsE69dRTef7559cR8r799lvquh4NBoORbM9/mzZtYp/u3Tmtkc2HnzJ3e37xxRecPn0627Rpw2g0yjFjxoQATMj+jkAg8NawwYO5n+M0afq81LbZXIiQDJye2x6BQGBJPt+jZ4wdy1ub2CgJOw4vU1UGPY/77bcfp0yZwpUrV/LSSy+NO47zIwBjV47sfGCfMFiWq4qVYl81DFqm6VdXp6/Eqqqqouu6zD3jTZL77LNP6q677qrnpWbevHnsmYdF0E37MdvTcXj3HXdsiz9kyBC++OKLPP/882PIciGZ+Y5upmluPfiAA1LNFYV36To353TUn22bV6tqygVCClDPoQ3S8o+f7Z2wlmbMmMH+OVuj+VYFAUninXfeWS/9gAEDqpGncxUL9vZo0LLprQWplLKuSOFmWiIRa9O2bcxx0prMrVu3wnEclJSU1Ivr+760//7719OC9e7dG8sjkTrauu99HxfGYujj+7jgxhtxyeWXb3vXp08ffP/993jllVeSAN7Lzovkd5FIpOeiJUumVimK/+Ghh6JTMonDFAXHKAoOURTsnUzir0JUVQN9E+TjeT6Luq5Hfv65nryGVatWcVEsFh8cDtfMSiaRytSZJN5LJjFGknCMEPBtG5MnT66XfuzYsU4gEDi8geYECtSg7dDIJgkHePq6LH+hTYWNjkMdiJqmGa0V6BKJBFu2bMmvv/66Xq/u0KFD4uabb663UJ07dy4Drht3gEgrIapKgRoTqDYUJbF48eJ6+RxyyCHs0aNHzPO8mY19p+M4GxYvXsz169fznXfe4euvv8558+bxb3/7G0tLSxc2lta27YdOPvnkaLagtWHDhlrfo0cAODMAfCsDSQeIKIBfpuv+LbfcwrVr19JxHOa7Zvmqq65KmqZ5746O7ILBbqSgPS2gZkEB0mzccThYlkMuMM3zvJeOP/74SCSSNjO7++67uc8++7DWHCgWi/HOO+9M6rq+0XXdcLY/1NWrV7Njx441SG/1tQTQDemtUzUQCPz9lFNOiUaj6Qtufd/nX//6V1qW5eu6fisasf8mCcuy7h8/fnwdZzy+73Po0KEhSZImNtEWnuu6C3v06FF9991386qrrkqWlJSEbNu+MyeeifQxJMeyrDsMwwi1a9eu0nXd1BVXXFGHZa1du5aBQCCMAu7R3mlgN9Z7ABzlAKEZjVwcus62OUiWQx7wDtIWqXYgEJjpum748MMPr2zbtm21ZVmbaz/ccZxIIBD4CEAnSZKOM02zslevXpX9+/evNAwjYlnWtch/JswNBAJve54XPvzwwyvbtGlT7TjOcgBdC/zOctu2V40ZMyb89ttvc+bMmRw8eHDIdd0v0cgZsaz0CoBRtm0/ahjGXWjCB0smjQ2gB4B9bdv+eciQIdWPPvoor7nmmmQwGAyZpnn19mKz08HOvO/vAj+2FaL6Pl33P7IsfmlZnGWaHKkoIQOIuOkb9ZScdF2QPqLaD2m7cwNAT+TYWiG9uzcMwLFoxE1UVvyuSFuOHpivUzSRNqiq6hWlpaWLSktLP5Mk6cJCgN4ZAWml3vhgMPiSZVl/LbCzFAS2yETeKZTRHB0SACbJwD4+oEnApq3A//jAM8yvVtxNu4gKBrvQDfLdtOupUGyKAZski/MFuZt2CRWKzU7xXbqb/utUEDY7lWfvpv/dtMM39u2m/z79Fjf2bUVGxEf6Zlzm/K/3rtB4/z/l0UC83zqPrbt86bWb/nfTTrksfTf936DdYP+OaDfYvyPaDfbviHaD/Tui3WD/jmg32L8j2g3274j+H+2F23j9+rhBAAAAAElFTkSuQmCC\n", "text/plain": [ - "
" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -327,7 +300,7 @@ "print(\"Note: This density is meant to be equilibrated with NPT MD. It can be very inaccurate!\")\n", "out = packmol(water, n_atoms=100)\n", "print(f\"Guessed density: {out.get_density():.2f} kg/m^3\")\n", - "plot_molecule(out)" + "plot_molecule(out);" ] }, { @@ -341,24 +314,36 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "id": "592366e9-f7ec-495e-94e4-9bb595d56f40", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" + "" ] }, + "execution_count": 10, "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ "acetonitrile = from_smiles(\"CC#N\")\n", - "show(acetonitrile)" + "plot_molecule(acetonitrile)" ] }, { @@ -371,7 +356,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "id": "07ab40ca-f03e-476d-9c12-dd199dedc73a", "metadata": {}, "outputs": [ @@ -390,9 +375,8 @@ "# MIXTURES\n", "x_water = 0.666 # mole fraction\n", "x_acetonitrile = 1 - x_water # mole fraction\n", - "density = (x_water * 1.0 + x_acetonitrile * 0.76) / (\n", - " x_water + x_acetonitrile\n", - ") # weighted average of pure component densities\n", + "# weighted average of pure component densities\n", + "density = (x_water * 1.0 + x_acetonitrile * 0.76) / (x_water + x_acetonitrile)\n", "\n", "print(\"MIXTURES\")\n", "print(f\"x_water = {x_water:.3f}\")\n", @@ -410,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "43a6adc2-aa9e-424a-bb76-745253e2dca8", "metadata": {}, "outputs": [ @@ -425,7 +409,7 @@ }, { "data": { - "image/png": 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", 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\n", 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" ] @@ -438,7 +422,8 @@ ], "source": [ "print(\n", - " \"2-1 water-acetonitrile from approximate number of atoms and exact density (in g/cm^3), cubic box with auto-determined size\"\n", + " \"2-1 water-acetonitrile from approximate number of atoms and exact density (in g/cm^3), \"\n", + " \"cubic box with auto-determined size\"\n", ")\n", "out, details = packmol(\n", " molecules=[water, acetonitrile],\n", @@ -449,7 +434,7 @@ ")\n", "printsummary(out, details)\n", "out.write(\"water-acetonitrile-1.xyz\")\n", - "show(out)" + "plot_molecule(out);" ] }, { @@ -462,7 +447,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "92c7e4ae-8b41-4f1f-8310-d87e3731829b", "metadata": {}, "outputs": [ @@ -488,7 +473,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "0e4c7bdb-6854-48c4-8f85-23547f228afe", "metadata": {}, "outputs": [ @@ -503,7 +488,7 @@ }, { "data": { - "image/png": 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", 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\n", 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" ] @@ -525,12 +510,12 @@ ")\n", "printsummary(out, details)\n", "out.write(\"water-acetonitrile-2.xyz\")\n", - "show(out)" + "plot_molecule(out);" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "id": "04967986-f2e9-4aa5-aa78-cf008dd6b9be", "metadata": {}, "outputs": [ @@ -545,7 +530,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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\n", 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" ] @@ -566,12 +551,12 @@ ")\n", "printsummary(out, details)\n", "out.write(\"water-acetonitrile-3.xyz\")\n", - "show(out)" + "plot_molecule(out);" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "f9288c94-14fb-4b32-a351-9441bece54f9", "metadata": {}, "outputs": [ @@ -585,7 +570,7 @@ }, { "data": { - "image/png": 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rwWzzyrkF43Q1x9eT6axFMc1YEsfhayJA1wseiCKacxwefvll+yuvvNL96NGjtwEoWr8ZYx1jY2P7tWjRArfeeivuvvtu7Ny5E6uWLcNeSULn7Gzs2rED7TuGFjenpKTg6NGjummaC8vtoBCoNLMZYwmcYG14Kb8xanT6ErK9DgCATB35GSvxw9LncObsRdw6ZEBBAYIAQRBgGIZWt25d8dXp01ErJweHLRYYjEECIDCGcE0rVdBSGqKiopB75Qq8y5djuyjipGmiSatWcBV+TKGmcIfDAVVVsXfvXrRu3RpEhF+XL0e1QCBO44S4xB47YbEXaUi5uAYv2NI2tLeNUk+WW6ehgQA2//47evfuLR89erTk2bJ2x44dzR9++AFvv/02li1bhuTkZPL6fOyUouBWIny+fDm2bd+OevXro0uXLkWnFlVVsXv3blq+fLlP07RbiOiGNW+VYnahudDq2DpPcnENnrv2HSfAWXUI7NFdsWd7d1SN/wPt2rWFpmnQNM2w2WwHxo8f32L5d9+JGwwDMn+tckfmOKiqeo30KBQ0TYOFMbyr61AZw8+6brpMk+vatSsOHTqETp1K157yPI8777wTffv2Rb9+/XD+zBm4jxzBJkHAx2Ti7V13IrHX3iI9PGMcRMkJawV2UVYAXo8Ha7dtUwEcL/H6xLZt23iPx4PnnnsOPp8PjRs1QhVJQh9dR6tmzTCofn0AwOnTp/Haa68hIiICcbGxOHrsmMbz/HZN0yYS0cHyaxIalTp6AegpWav/3LDvsTLNhdxZ65F/8hG88Nwk/PHHH/jpp5+2+v3+W61W6+oagUCrI6Uw9HZdB3fzzejavXu5ddm2bRutXL7cDVUFAwwG7DCt1k5333132KpVq7hJkyaFlJkDBcevT+fNwyRdxxRZhpUxmERICgLWDj/DFvXndJp58Ancc+krvC6VrXl8QFWxVJJUk7ElLpdrVMlNlMPheNtqtT7cq1cvbN26lbuSlWVpUq8eu3XECFit1mtoBYNBLFuyBKeTk+FV1bNuoppllf23HL14MXxCTO2JtvLMhezRPeEPMJw5cwZr1qzx+P3+N4kox+fzvV6dsVLtvB8HsGvDBhhG2Zoz0zSxfv16r09Vh3qJwj1EkW6iAT6fr80PP/ywKysrS//+++/JNK8zwizKv3XtWrQD8FIhowGAYwzjOR2e819fkz6s5iP4VCcEyhgUeURYFAxqeW73yNIYDQBut3tyZmbmTd99993j6enpyTWqV2fDx4y5jtFAgYh02MiRqNGoETRBSCyzQwrwdxy9+LpKeLNyvyDGGJTwxli6dKnqcrm2Avil8FX22RCd1o3nUS8QwNJFi2CaJogI+fn58Hg8uNp3pmli8eLFQY/HcxjAuuL5iSg1Ly+vUyAQaLl///7T7777rv/IkSO4ynTDMHDo0CF88s47EI4exRKeR8mPtjoDuGDGNc8sjgaQY/tgWNCAWkrdfUS41e/3CcCXRPRjqGMRY6y90+n8wOFwvKooSrteN91U5uzDGEP/W2+FAQiMMUfIhJVA5TZojKnlqIGLEPDlIy8/44Cu67cVO4ZsOWeaxkHDQLMSazZjDD8JAm45fBjvvv02iOfhdruhaRrq1q2Lli1bYsOGDd60tLTjwWCwf2n206Io3mm1Whe2bdtWO3ToEPvPf/7j1XVdsFgsvB4ICA0FAS8QYbggQCxldjpHgClfv0mMbfMVtqxvi7reVO1pSRJ68zwzAfxmGOZbqur3EP3qAUJqoRhj9RVFWff+++/b2rRpg08//RRLlizBhAkTio5hpcHpdKJWrVrGiRMnbgPwVciEFUSlRrape1fnp60IqUm6CkNzwZu7Vy1kdNHXQUS6Abz5YCDg9ZcyAMIYw1zGkJOXR0OGDEF2djZycnKQmJiI77//Pv/MmTMTg8Fgp9JsuBljEYIgfLlt2zbrxo0bwzMyMixt27ZlhmG8rmnaRkmWiUkSBKBURptE+MgQYEsaBQDwu44g4+iLSEu+HxlHJsMVzA6cJ3r2xWDwt/Y+X0Ynny9tRjC4LIOorwe4i4i0UP3BcdzwRx55xHL33Xejfv36eOuttxAWFobz58+X15VITEzkAVTK/ChkPSqTmMzAh1cu/If0YHaZ6XLOfk6Mk9eWphgIAm+kmOaqdj6fd6Wuwyhkeh4R5qoq9fL7fYFg0Jw1axY4joMkSZg5cyYEQXAR0RdEFOpjq1m1alWtRYsWAABRFDFy5Eir3W6/r1OnTp03bd3KXl+yBM/a7fhRu54vb2kmfEoCBLkKzm7vgQu7+6JRtWT0am+iXaNMOMN4S1hY2Bt+WRZcQJ08ooR8ojuIaEcZUzdXqArlLRZLcb0CZFlGqH3FNf0VDAIFzgt/GZUySyKiiwzsw1PbbvHqaunHvfz0lUg/NsNr6vlPhaBjuoFhh03zq5HBoBnm8SDW4zHjPB5tWjC4IihJX4miyBcXIZ47dw4cx+WXU8W09PR0OT39T++izZs3B71eb9Ly5cuV5s2bo0+fPnjn448xz/Kn+VmqaeKhQAAzdSC88Wyc3d4TPTpVxczpL2DwoAFo2bIlDh48iMGDB+Gjjz4S+vfvf5Pdbk+9asbLGLMyxpxXxaSMsaaMsWcsFss+nueDgiAEJElqOG/evMDatWuRm5uLt99+GxcvXkRSUlJ5fY7k5GQAiGCM3RXKdLiiZkmVdv9hjHEcb50LJoyNrvmQ4EwYKvKCHQHPCWSlvu/xXtmhk+G/mYh2lUGrq9PpXLVu3TprbGws5s2bZ86ZM8dLRHtiY2O7Tpw4Ufj0008xbdo0+Hw+PP30036XyzWUiMq0e7LZbC86HI5nR48ebTl06FBwy5Ytl/1+f5LP5ysyeFi5ciXuuftu0+JyeQEgj4gR8E2As9eTBK3HoEGDWNeuf+qWd+/ejcuXL2PNmjVgjME0TTRr1gxHjhxZ7ASaeIB6QsHxL6AB6yx2+80jR46UMzMzuX379mH58uUYN26cb9++fb/Istw5GAxGWywW9OnTR+7WrVuZfb506VIcOnQIt912m//QoUPGsWPHTrnd7k5EdI3S5W/z4izcGE1kjH2Yffqjx7PPfDYAMGXGhHRDy50HYBERlWnwLcvy7U899ZS1SZMmmDx5Mr788kuuYcOGDk3Tet5999145plnULt2bSxZsgRbtmzRXC7Xi+UxGgC8Xu8sxtjO2bNndwCQA+DrsLCwpWPGjOn23nvvyVlZWZg4caLP4/M9nUu0DgABOEdEAcZYZ0UJX925c6drzkJ+vx+1a9cu2rlzHIeaNWvCdfToHR9YLNwtPA+eMT5F16V2pnnb+vXr0bZtWwDAtGnT8M477+D999+39unTp31ubm4iADDGZvz+++8vNWzYEDExMaW25cKFC/jjjz9w7NgxJCUlKUSEQYMGNVi1atUEALPL64vScMPGC0R0DMC4G8lrGIb/ypUr5sCBA7msrCw8/fTTcDgcWLt2LU6cOAEAGDp0KAYNGoRC9WOFfayIaC2AtVf/M8aGrly58qslS5b0EwQhaJrm65qmfVhynZVleVynTp0sy5cvx6VLl2C329GnTx/UqlULCxcuxOTJk1GvXj3s378fmzdswA5F4RoVO1Ek8jwMXS9iNAB0794d69evh8vlAmOsuBzuc13Xn/nggw+k3r17o02bNkXKn2AwiL1799Ly5cuDSUlJSEpKshS2A/3797ds2bKlUSW6+rrOqdAPwMaKpq0ArXqSJAUbNGhAb7/9Ns2dO5fmzp1Ls2bNotjYWHrsscfoyy+/pJ49e1JsbKwhSdKH/62yQ/2sVuuO6tWr06hRo2j79u308ccfU3h4OD333HM0YsQIstlslJiYSBZZps9lmcjhoCNWK00SRRrI83QHz5NTFOnHH38kIiLTNGn8+PE0aNAgqlq1qlcQhAdK9MEgSZJ8drtdE0WRqlSp4ouLi8uTJMmvKMoaALcqiuI/efIkERHpuk59+vTxcRz35I3y5n8l8gJjTBBFMeeJJ54IK2lS7Ha7sXHjRni9XsTFxaFp06Z47bXXfLquV6EQxn//DSiKsjcmJqbVmTNnwBeO2GeeeQb79+9Ho0aNkJubi/Pnz8O+axd+4HmMDQSw3jAwVhTRhuPgB/CDpuE3w0DDxo1hCgKdPn1a53n+lNvtnq3r+sJS+kFBwbFKBRCPgg3zaSr0YZdleYrFYnm9b9++7OjRo8bFixf3ud3ubkSklqDzX1+zyxda/1m4BUBHFBwZdpdyLu4THR19ne04UKCZGjRo0DXPatWqZZ44ceI2xthOAI1R0CmpdAMWlqGgquoBRVFa8cWm5sjISOzevRtHjhxBo0aNsHfvXvTQdYzUNIgAzthssBQ7sw8XRRw0DPQ9ehR5RB4VqFJyM1Uche9OFP49W/wdYyzCZrM9OXjwYHI6nXxGRoZ+4cIFGQX7jJKoEG8qw+xr5K+F58e2NuA+CUhQgUwv8DWAY3a7fXtSUlKs1Wqlo0ePaoyx7nStPVdSQkJChctOTEy0nj59+lVBEKKqVaumcRyHS5cu8YqiXAwEAjMArAEwHAWmveuI6I9KtAsAYJrm7IsXL97/wQcf4OGHH8aRI0fw1ltvoX79+jh79izy8vLwww8/4I7bbkNEIIBVVus1jL6KZjyP7y0WDPb7FRUoVwBVBkbcdNNNzq+//loorJ/UqFGj2sePH++OYnuSQlRINn5DGzTGWHwYsFJhrN6joqjU4DjukmnSR5o2PJcx3DF0qPzFF1+IjDHMnz+fJk+e/COABsVIaIZhVNi0Rtd1rlmzZokjR46EIAgFvmCmiZSUlPqLFy/+TFVVbuDAgUhMTBTmz5//siAIY3VdL9W4nzEmosBMWQOQS0TEGONlWZ4bGxtrvPXWW/zjjz8Ou92OgQMHom3btgXKk61bcffdd8MEENuoEfpeuoTbDQPvF9C8pozuPI8qjBluot4o+BDL608G4A4AIwRAYcBhABFJSUlFwhSO45CQkEDHjx+/YTn5jZyznXYg+UlJin9ZkkS+WEOJCO9pGt5QFOw5cgRVq1ZFIBCAzWYzTdMssohkjDW3Wq3bZ86caeX5slWHRIQ33ngDt912G+oX6nyLY9GiRejduzdeeeUVAAX24m3atAmoqprIcdy9kiS1ZYxxwWAwSxCEWMMwBguCANM0OcZYnq7rc0zTVBMTE2c88cQTtkJDC5RWrzVr1iAjIwN79uyBy+VCz3btMOncOYwqRb79eCCgvadpLxDRm+X0K+dQlH3O6OjmgwYMwI7Nm6GdPw+vx+PL4jjlw4ULWf/+/XHgwAEMGTLE6/P5GhDRxdJ4U2ZH4gZGtgRMGigIsdNl+boWMsbwuCThbCCA16dPx3uffILdu3dDUZQcj8dT/KtKMU3z7OHDhxsVN2EqDWfOnIFhGKhbt26p771eL7oX04EzxkBEMsdxGQ0aNNCaNGmipKam4ujRo+jatSs6d+5c5PV5/vz52PXr17+UkpJiGTx4MHfVdi3UB9izZ0/MmjULx48fR/369THywQexZ+rUAse0EqCCtbVceagFWGa125sfPnwYYWFh0DQN3Vq3xu3HjlnXGQbuv/deSCjYwSlAMoAEABfLplo6KmupwluBCc9KUkh3VwCYxPNo9MUX4BTF+Pzzz4Ner3dMYX5mtVpfEQThKSLC4sWLyWq1slCMdLvdWLRoEW4qQx0YExODb7/9Fn6/Hy9PewOpqecgh3diksQLp1L3CHn5Pvi8eZgyZcp19mlJSUno27ev9dy5cyE/puIQBAGtW7fG119/jVmzZuHgH3+gga4DJdyRiAjLdF0FUObegTHW1gHc1LllS4SFFXgXi6IIa1gYPtF1TJUk/CaKcDCGXCIs1LQO04PB9TJjk4JElXY4rPA0Xli5hHDgZJ7DEdr8sxA1PJ7geeALIvqMiPYXNuSBmjVrvrtlyxZbbGws3nnnHZo6dSoGDBjA2rRpg6siTV3XkZycjOXLl6Nz587o169fyHICgQBef/1NePwcqrX4EGFV+oNxBd+wrubh5IYGGP/IGFSvXr3U/CkpKdi4cSPGjauYfGj79u04d+4c6teogV2//IKdjMFZYs3+Rdcx0u8/5wZqlhTeFEc4Y9+NEYQ7Fzsc3L6UFFSpUgVLly7F+DvvxB5FQWIpH3iqaaKN1+vLA/oR0dYKVboQlQlnuRHAKFb61v86cECQiD4logNXn4WFhd0+depU21XDwsmTJ7NXXnklsHz58n3Lli1rFRcXF2SMISMjQ2KMpcXHx1ft0aOHsnv3buTk5EAURdSvX7/IzSclJQWLFi2C2+1GWEQNCHKVIkYDgDdnC6KjY0IyGigYrVopWrBQ0DQNx44eRebevdggy9cxerOuY4TfH/AA95fF6EJ0f1iSuBhNQ+uGDdG1UyesXb0a70tSqYwGgNoch1dlWXkuGHwRwM3A37dmZwQB7ahhoFEZG6s000Q6kQggtfhzXdfz09PTCQADAI/HA7/fzwWDwSEAlPPnz9dHwRn6FIDEnIyM1a+89BI6CgLa6TrcHIdFa9bAGhmJ7rfcgsWLF2PFihXo0qULVqxYgXtGD0WNzrsgWuIBAN7sdejYqmzpYkJCAtLT0+F2u+FwlL/RPXDgAEwAcU2boumhQxgmiuhgmvAB+FLXcQIgTZaBYLDc2Y8ATmQMLzCG/sEgtq5di981DbeFsI69ilGiyCYFgz0ZY2FUiXCeldNnE+kEfDhbVcs8P76rabpQoBBxF3/ucrlmT5061ffBBx/Qr7/+in79+vlEUVxCRNlEdIGI1hLRaiI6bQOGVwsGpWRRxCqexwxZxhxRxHlRxNO5ufjmyy9x59Ch6N69O3iex2233YYOHTrAm7P9zwJNT0iz4quwWq1o3rw5duwoX/yekZEBl8uFqVOnIi8QwJSXXsLlHj3wdpUqmMZxNPmrr3DF72cbN260WCyW78szJ+I4LrCl0OauFc+jC88jieMglWPj52AMYYxpAMoKL3Z9eZVJDAAB4J0lup77pqoaJWcpIsIXmkbvq6rbA0wvmZeIDvh8vu4vvvjiqnvvvXfX/v37X3e73aNLpmOMtVUYG73VamU1SkxnPGN4UBQxBLjOlSg/Px+sWKgUTqyCK1fyym1Tr169sHnzZhw/XtICuMirFG63G9988w2mTp2KkSNHIj09HeHh4ejTrx8S69Wjdt26sZEjR0IURXTo0AGxsbEmgJBWoYyxMA9jVeeJYpEBRzhjyCp0nCgLGhHcBTNnpYL03oiKM5sx1n5mMLjmfVWtM1GSOAsRUomwXNfNy0RpPqAvEZVqc0NEewHcUlYZYcDkKaJoKbkeFsebkoQGq1bh1VdfRe/evfH9Dz/i1OnLqNahx590qg7Dzl1fon//fiGPUwAQGxuLm2++GfPnz6fOnTtrXbt2laKionD8+HEsWrQIPp8PHMdhyJAheOyxx7BkyZKinb1hGNixY4fRp08fQqFFSUZGBjIzMyUAGSELBZwOh0MNr1dPGH/0KD4kQi3GEMMY1hkG+oZwYQKAZboOC3DIT5RTBv3rcMOKEMbYsPr16y+0iKJy/uJFVKtWDadSU+H3+183TfO5MkiVizDGMvfYbDH1ygmnMcTnwybFCtFWDZKjBSLqvAbRcq3B4JltnXBTz7ro3Cm0a41pmvjoo498p06dmisIQgSAB2VZ5k3TZCtXrkSPHj2wbds23HLLLejXrx9Wr16NMWPGoEaNGti+fTutWLEihef5sP79+0c3btxY/uyzz3z5+flveTyeaaHKZIwJNpvt0ty5c2MXf/klzhw4gIc0DVmmiVWGge2KAoUxcCU++DwitPR6vWeJ7iWipYW0/ruxS0up7MOdOnV6NxAIyBs2bEBYWBh27tyJXr16Bf1+fyIRXWOoJgjCKJvN9rqu63ZRFNfn5+ePoYJ4KdfBztiVozZbRFI5zL7f70cmAXui+yKuw9LrxJYA4HcdxdkdvTFs6MBSned0Xcd3330XOHTo0OFgMNglLCzsx06dOvWqUaOGcvnyZSxdurQo7ciRI5GSkoKBAwciKioK+/bto++++86jaVpHAJcAjOF5PtowjG1UAWMLxlgLq9W6JiIiQr5y5YosqmoGZxj5uizXVU3TAtPEEKsVnxPBBmC9YWBcIODLIFroAR6rwG7/WlRCB72xxP9mNptNe/vtt6k4GjVq5ALQpUTagdHR0d6tW7fS+fPnadSoUYbdbtdtNluWKIrjS5YVAez+wWIhcjhC/ky7neoxZozlBaon2imh9kRqPsRHLW4NXPNrOjCb7JFtSJJkio2NpeHDh9MTTzxBEydOpL59+2qKovgsFstvKAjzHG2xWIKBQIC+//576tu37zVt69u3L9100000bNgwio2NdUmSdBFA04r2YYh+tQBoAqAaFejVp7Vq2VLNyMggj8dDw+68kxKtVkpijOIZM6UCP/BuZfEmZFk3ymwiAsdxa0aMGFHUGfn5+RQWFhYE0KhYPi48PHztO++8Q5qmERGRpmnkdDpp7dq1VLVqVS/P8yNKlHVXG45zm3Z7SGZvs1rJDmQrgOstSaLWooMilARKbPwG1em6nup0WU2xtR8nXnCQzR6piaLoE0UxxWKx7FEU5YTVaj0sSdInABoXKzfebrf7DcMgt9tN9evXp0mTJtG6detoypQphs1mUxVFSVEU5RcUnHG5v8Lo0n5OpzN9+/btRX3qcrlIFgQ6qChk2u20RlHICrgBVP8fYTYAZgPecQLeOKuVRo8YQe+88w41b9CAqlksqh04CaC2ADxrBy7bGTNiJIliw8LouSlT6OLFi+R0OikzM5N++OEHioiISI6MjNwdGRmZYrPZZgOwOoCjT4miWhrDT9tsFMeYlwPelGVZj5RligLoDkGgRqKNwiQniZKTkhJrUP/+/UmSJD+ARyrQRuZwOJInTZqker1e2rJlC0VFRekRERFnw8PDFwGI+28zt2T5EeHhwVWrVhUx++LFixQmSVS8Hx4XxaACzPkfYbYCPNOA4zzZdjtdsdtplizTBLudvrJYyLDZaLIoanZAHcTzvt1Wa1ElU6xWesRqpQiLhbp06UKmadK3335LERERxvfff09btmyh7t27+xwOx7cAYh3A4QaMuZ6VJJolSTRflmm8KAasgF8EnlMUxb9jxw4iItq4cSPFxsZSs6ZNaeTIkfTGG28UmTtNnjyZBEHwALAWtiUJwBTG2PMouEyleDurhIeHb+Z5XlcUxSVJ0uN/J4NLlN0sjLFA9ZgYWrlyJW3evJnaN21Kz9ls13zsJ202sgDuv53ZACwK4D5eogLFf7fwPE0QRQo1Db8vy1QrLo7WrFlD0dHR+qOPPlr0JXs8HhJFUStcQ3mbzbYuPj5eHzhggBkZGWlaZXkbCkx5ujZu3FijYpg9ezb17NmziMnFf/Xq1XMDuB9AI0VR8u+///7gpEmTNLvd7gUwoLRRVtlRCUAs9j8mPDx8taIobofDkcYYG1YBGgO68nzeIouFukZEUPuICHrdZiOjRD+adjvxgAHAUhlmV0aoclWPOKQVz1OoY1GKYWCfaeItWS51dwwAj0oS5MxMDBk8GF6Px9uzZ8+id4qigOd5EwVXOw2Ii4trd/LkSf7nX35hx44dYxabrQWAKgAsFy9eFDyeP83SkpOTS/WKBIDOnTvbrVbrAxERER/Mnj3b8fnnn0vvvPOO8Ouvv1qtVuvCkumpsBcrAovFMkWSJB/HcYHw8PD9jLEkh8OxbvTo0T1Onz5t//XXX+OdTucCxljvckh5c4nYcFHEZl3HTl3HMxx33fHLiyIFxVVbtAqZJd1ItKRaHTmu9B4FsEjXMUoQyhX5TRJFJEZGggwj7PGJE3H+/HmoqoopU6ZooigeRUFs7wYDBw6Urzrox8TEoH379oIEbLIDK3RdR7du3fDuu+9i9OjRWL9+PTp37lxqeYU67Ggiqt6xY8eiynXo0AF+vz+yjMA3ZYLjuGExMTHTU1JSLMFgkJs8eXJTq9W6WRTF2nPmzBHj4uLQpUsXzJw50xoeHl5q4Nxi2JlqmlxqoVtQtmliAhEGCAKeJEJ+4ff3raaRA9hAfzo3/m3RkgIuQA/18rJpomYFYotW5zhIHIfhI0fCl52NOrVrQ1EU+uKLL/a43e6bC0fWmXXr1mlXtVJutxvJe/dKX8qy9aLdbjH8ftSvXx8//fQTsrOzMWHChJAj2+/3AwWhJ/f95z//0a4O3G+//ZbsdvslAE0YY/bKdobT6Rw5c+ZMa61atSAIAl566SVeFMVYIuKKTw6GYYBK8TwtDipwgvzsqWAw4DJNdAXAjRqFhxcuhOf229ETQLppYrqq+vJvwFHgRpi9ZrGmGVqIWe40gIsVmAHTiWCx2dCiZUtER0biM46DaJrB/Pz8AUR0NWDa1jNnzkgtWrTAI488gmZNmmCw34/hkoRwxlBfkuDxeFCrVi1Ur149ZJQkANi7d68/GAwuyc/PHz9//vzzTZs29bRv104dP24cs7rdkTUY22IBMh2MLWSMhdaJloBhGL7iMvrs7GwEAgFe07S0hx56SE1NTcWqVavw0ksv+VwuV7kGB37gxbW6fryP36/VatsW7370EQYPHoxPFi6EtV49tPH5Ai6ihUS0uqJ1vIpKM5uIDpvAsU80rdSvVJNlLCAqEu6Hwqcch8bt2wMA2vTsiSWCgEGCYHLAvcWSTZckSRg8eDCsVivio6NxhQgqEaarOlI1Dus2ncTPa9Kx6Mc/8PLUV7D0p58RCFzrQ+5yuXDgwAHOMIz5RJTp8XhuPnXkiBq1bx+3hTFkOBy2M3Z7WKrNpjwmSaOswAHGWJOK9IfL5Zr70ksv+d566y0MGjQIiYmJcDqdYlhYWK0lS5ZILVq0wF133eV3uVyPE9Hm4nkZY7GMsRaMscaF5tcgIp8b6HzANHdGF3P8Y4whKiGBMoh+8AATKlK3krgh2ThjrL4C7HpNlh2PiCInM4Ztuo6ZADYbBqwch0mmiRdCqBeX6DrG8zyemTYNgiDg7NmzWP/pp3jQMPB8MLjARXQ/ADidzpNz5sypM2ZMwd0xgUAA0eHhaEISTjvbILLJW1DC/+RJ0HsaWSdmgQtsxeMTH4bNZoPX68W8efO8OTk576qq+gIAhDN24BlJavy8LJc6FXytqjQuGMzwAo9GRkZOJSKrqqpLvV7vS1SKHzZjbJwkSe9369aN69q1a5GNm2maOH78OFauXInMzEwiVfUTIPGAn5PlfN00Y8PCwgK6rjOfz8cAfK5p2jtEdJ4x1tRut+/aunWr0rx5c+zcuRO9e/f2+Xy+ZkSUWqL8It6UycNKbDpLNrCeE1hgAi0b8Tx3SBTlQbfeilatWsHn8+Gjt9/G7ZqGp3keV9WUWaaJDwwD7wF44LHHkJhYEC4kNTUVOz7/HMMNAy8Hgx97icYBQGRk5J6PPvqo9V133QWgYN1z2GwQuVjYbTUBTgJXpT8ikkYVxTgnImQcfQoOtgkN69cw169fH9A07TNVVb92AJMAdLUyVu2Szcb4MjaRzbxe/2lF4b755hs5NjYWTz/9tO/AgQNLSqpkGWNJoigeuf/+++2F4bOvg67rmD9/Pi6eO4eOuo6DioJeAwrcga+aYmVnZ2PLli3a9u3bA5qm3UxE2zmOu0sUxQWyLENVVTMYDA4nol9KLaQiPLuRkV3i+VBZlr+ZNGmSFBf3501Ibrcba3/7DX/s3Ik4joPI8zgXDKJ69eoYOmrUNd6Lv65YAWXbNuxT1cAFoqlENBsABEG4Oy4u7os1a9ZIsbGx6N2hA06npuIBUUFXzkQQwCJTxFpDR0yzuYisXsAH0wjg8K8JEHjtV1VVX3EAT1gYG/CEKMoppsnX5jhMK8eo4TNVxX+aNMGmPXsAFCwFkZGRhmEYViJSGWNtIiIiZgaDwZatW7eOveOOO8o8fvh8PsyYMQOGruOFF18MGZ05JSUFX3zxRVA3pSwyfAkAgfG2NDJ8bwH0BZVimVLRkf2X78+2WCwPDBo0SCzOaKDAjccRGYlet9yCH3buxDfbtmHLH3/gUk7ONTtmXdexbft2HImJQfs77pAjIyOnOhyOFwvffZOVlfVM69at/UkxMYg6cwbpNhvmyDwyGMNjAH7TvWhmE+E5+DjyLnxf0Cjegsjq96majgMOYFxbnh9w1mazPi/LvMQYEipwOVwCxyHg/fNyFqvVejU8mMAYa2m1WjdNnz79ZkEQqnTv3r38mwytVjRp0gThTidOnSo1ujWAguD63bp1l51VeiQ2H+JhzYd4We2OyxLC4m6ZxfHWE4yxeuVWPgT+ErMZY/GGYfRo06ZNqY29cuUKhgwZgtatW6N169Zo06YNqlSpUmRhYpomvv36a1htNhxJTcXixYvZkSNHrBzHPc8YawAAwWBwrt/v72Ulol8UBTbG8JOm4e3wcGzbvx9evx/9J05ErCIgO/lxkFlwKlTCGkuMSc0IGLpcUaxXQ2DFMIbzFZjNzpkmzl68aJ47dw4+nw+PPvqoarPZdhKRLzw8/Jnp06crffr0gd1ur1D4aABo0qQJBEHAkSNH4PF4kJOTA5/velf2rl27wJuzBabuA2M87NFdUavDj7aqTWbHcLx1C2Os/JifpeCG/bMLXVaGSnIY/968L8AYQ/Xq8ejapT3i4wsM/iIiIrBy5Uo88MADYIzh5MmTSE9Ph7Pw6165ciWysrIwYcKEorUrLi4OzZs3V7ds2VIHwDEACAMmTJIkUgpFcj/JMp6fNQsNGhR4FL08YwY+/+gjVAuqyM/4Fc6qg2HoXghmoMZYUeTshYzWidCL4/BAMIipkgQhxAgnInykaXDpenq9evWiDMMQ7Xb71vz8/DsBgOf5sPj4eHY16Kyu68jJyYEkSXA6nSElh4IgIBAI4NSpU5g1axYURYHP50P16tXRpUsXNGnSBBzHwel0IjIqFgHX4WsC8EXXHMu5s9Y58tN/HgugTE+TUsuvbAagwMOQE8JWcry1ZVj1CaI1siNAJs5kr8W+eZ+jfv1aGDWywBhw/vz56NixI+rVq4dffvkFjDH9jTfeMFRVlZ988knUqlULX331FXRdhyAIyMrKwsGDByUUHNkBADzQro8gFM1CsmnC5fpz6dI0Df5gELdBxff5yXBWHYzcC9+4baSjC2+RAGCtrmMkEZgoIhAM4qVgEK9ZSvd1+FjTkE8E2TR35hvGnQBYXl5e0VHT5XL9OH369G7Lli2zZWVl4c0334TFYoHL5UKdOnUwfPjwUs2g0tLSwBjDiBEj0KBBA3AcV2Qjv3r1auzduxf33HMPBEGAKEowzevtOmPrPKG4L695kjH2VmVEukDlpvHuQIFPMSc4NkdWG9G68c2plip1J8Me1Qn26C6o0mAa6vU+iQtZMViw8FsIgoCHHnoIjRo1QjAYREJCgubz+b50u93j6tWrl//qq6/ivvvuQ3h4OFq3bo3Ro0frTZo08RqGMYeIjhYru6hVJhFaqiqmPvssJkyYgFWrVuHu229HZ8ZgA8DA4L2yCwHPCZ0BaUEAF00TIwD88NtvuOxy4ZdNm/CBYWCk34/kYhEVT5gmHg0E8Lqqogpj3nzg50IdyzUyBV3Xv7h06dJ7zZo10xljeOmll3D27FlcunQJ4eHhWLfumnh8BfU2TWzbtg2jR49Go0aNijxcrnqZPPHEEzAMA99//z1M00Tulcwik+jisEa0A5nBaBSES72GN+XhBmTjbIzV2bpWQrO5Uml3RnK8BYktv8GFNB+OHz8OQRDQvHlztGrVCsePHzeI6GUAm8+ePSvl5ORAEAT89NNPkGVZ/eqrr37OzMzs73a7XyhOUwe2/qrr+gJNQ12vF59pGgabJk58/DHu6N8fJ37/He+ZJpbBAl6Oxukdt/rICIzNA375Xte9uw0Dndq3R48ePQAAXbt2xegHH0QOEQb4/YhyuxHv8aCLzwcHY/jYYsHhgrhV35fWEUREXq/3ecMw4nVd18ePL4h3pygKJk+ejKuB6otjy5YtCAsLC+mwUHivGY4fP45t27aBk+JhcVzvyFi4RBCu5d1/XzbOGGOcYH+6Sv1nrKHWJQDgeBnO6o9jw8aCS2qICCtWrAjyPL+aiNKIKJWIPmzZsqX3hRdeoFtuucWXkpJyGsA9RLSlJD03MGeOqtLsYBDfKAr2Wa34j6Lgd0XBRbsdNzGGrj4fzul+unToab+h5T1AZCw1ga9+13UulwiX0tOviTuWlZ6OFhyHJnY7TLsdD0sSzisKevE8hvv9fn9BXcoL5+gyDINLS/sz3NuJEydgKbY8GIaBDRs2YPXq1bj33ntDrudAwZUVHTp0wJo16xGeVLqQzJeXDMaJLgCVjkJRqXM2gGGMt55vNjCn1IvPikNX83BsTQ3MmP4yVqxYoe7fv/98MBhsS8WiMDDGBjDG2hTKwr+kEFEKGGOtIoBdKTabUCWEkuWJQADzNe2MF2hJxQwZBcbucwIfVLPbrU3798fQUaOwdf16LPrsM3iIMHP2bMTHx2PGyy8j7dQpLaiqmW5gLBGtKq9P7Hb7vIiIiIcURZGefvpppKWl4Y033kCnTp0QExODrKws7Nq1C6qqYtKkSSh5PC0NFy9exAcffIKI2i9CCW8Oe3T3az6Qc3vH+vMu/vC6aQZnFOufCp2zK7tBs3CcRWeMlS2RAMALNui6imnTpqHQP/rJq4xmjMWjIIbIVtM0V5ZHywE8OVmSWChGA8BUWcanmhaPAj14EXSihQJjQZ/H80HmihW2fWvWSDZVRQDQX337beGqQ1+PHj1QpUoV3gDqVGBEgzEm8jz/SHJysrB69Wr8/vvvuHDhAjRN8xw+fNgeCARgGAbi4+Nx8uTJCgfOl2UZBA6q7zyunPsSRAbiG06FM+F25F36EXlpPwaI1I8rRKwEKsvsbNPwCLqaA0Eq2/Mk4D4GiyUMLzw/GZcvXxY++eSTbxljzW0221hFUZ6Mj48PpqWl8TzP32UYRpkMN4CBI0WxTK/9CMbQnee1VYbRG8Ci4u90okWMscX+YHBgejDYkgCKjIwclpiYWCTfjIyMhCiKumEYVlTs9lqZMQan04nhw4dj+PDhWLVqFfbt23dZURR28OBBW0xMDNauXYubb74Z+fn55d5pBhTIJmR7LSQ2extEBE/2JpzfNxaZqfM0f36yhwx/LyLKrED9rkNlfb18jLP8cuXcV+U6meee/wRdu3SAw+FAnTp10LVrV1kUxXmRkZETz507J6empoZt2LDBVugTVeb8ZgBSeAWkXhEFBgilKrSJSCeiZWaBOHaa3+//ctq0ad78/HwQEV5//XVDFMWzAHJLy18KPY/Vaj347LPP6qZp4sqVK3j22Wd9Pp/v08zMzA+SkpKCiYmJ7sGDB7s4jlu1Y8eOkDYAxbF12x7Y4+4GULAZc8T0QJ0uqxHIP8TI8A2hYl6xlUWlj16m7no94/hrgaA3NWRC75VdcKUtRufOHYqedenSRSSivmPGjLFdlYt36NABjRs31gE0LatgGfAcLifoPADsL9iBVejeJL/f/+aJEycWxcTEaA6HI/Dqq6+ecbvd/SpzdnW5XAM/+eSTg7Is67GxsfqpU6e+CAaDb3k8nmcCgUC1S5cudfT7/fGapj2zadMmrbhsoDSkpaXh+PHjiKh2bSwH2VYLMbUfBcfbQl1i+vccvYhoD5nBySc2dvHlXloCMv/U+Jm6D9ln5+P8H7fi3nvuumbaioyMhM1mM3ft2lUUwysQCODcuXMCCkJPlgrGWDfdYgn/oAzDBAD4wzBwsSAY+oYyaDVnjD3FGJsAINbtdj+oaVqU1+ut4fF46hHR2fI6oTiIKD0/P7+1rutOwzCsHo9nwtUzORFlEdERIvIR0UHDMN5+7733vKHuNbtw4QI++PBzxDd9H4LkvO59VI37BSJjRIiqVOjo9Vd8vW4SxPAfOV6wh0W1BMGEO3svatSsiQH9e5Z6npw+fbrb7/f7RowYEdG+fXtp4cKFvqNHj65xuVy3hRpRkiS9Pnny5Ke/mT+fzfJ6cW8pTM80TXTw+XwXiZ5UiT4JUf8hdrv92zFjxojZ2dnGsmXL/H6/v21J3fDfBcYYEwThaQDTGjVqZLZr186qKAry8/OxfccBXLhwHvFNP4Az4Y5S8xOZSF5uBQC+pJDnbwug82fhtNpisXzXrm27MfXqVecZY6hatQsiIyNLTV/o9irput7ym2++eWDp0qU13G73dtM0Pypr6jQMw5ufn2+s2rRJuLl7d/wWDGKcrqMZz8NFhEWaZs5W1UAAmFsGo5nFYvlqzZo11g4dOgCA+Nprr4mzZ89+F8DAG+2DyqCwjW8wxj49fPjw6FOnTg0D4DRNahDTcC6r33cYOD60/76h5YIxUTVNtfwg5SHwl27ZDQaDH+3bt2/kzTffbM3IyMChQ4fAcRxq1KhRFArjKvbs2UOiKG7QNC0VwPPF3xWa5KilGeSZprlw4cKFk2vUqOGYN38+N2P6dG1wcjL5TdMUAFUAfnMBb1PZge4kVVXt7QvNoACgW7du/FtvvVW7jDx/C4goF8Dcwh8EKWIfLzhacrwCLZiJK+cWIu/SUujqFfBiOMLjByOqxv3IT/uJGK+Ue/Yvr/BKe4QU/4mieMpms1FERAR16NCB7rvvPoqOjqbhw4cXGejPmDGDbDabF0D3EjSrh4WFHeR5XhcEIWixWJ4NUXZdp9O5Iioqar/D4fgQgL0S9a4D4CabzZa9dOnSAkG3adKjjz4aDA8P/6aidELQtjgcjrmRkZGHIiIi1gFoXeydLIric06nc7EkSTMBhKHAkaAPY2yWKIrvAXgGwGMWR0NPUqsviBcjKDJpNNXpspYa9j1KdbttouiaDxMvOokTwgMo4dBXHm+uS/cXG+tUFMUzevRoGjVqFJmmSUREJ06cILvdTq+99hpNmTKFIiIivKIoTi+Rl7fb7admzpypG4ZB586do6SkJC/P83f/lToVo9/bCewNB3ztOS6vJmNuRVGoa9euwaZNm7rtdvtJAFF/gT5zOBxrBw4c6Fu/fj3NnDnTtFgsPgA9AQgOh2PbTTfd5Js/fz4NHz48IMtymizLl6Kiotz9+vUzBw8eTB07dgxKkuQXBCmfFyOofs8913mhtrg1QA37HCFBjtUA7p6/0id/ySyJMdahTp06G9q3b2/p3bs3Bg4sWP6ioqKQlJQEr9fr9/l8AdM0n9c07eMSeWtEREQcycnJKZKzf/XVV5g0adLPOTk5gytYp0QAVwOYnaTCyH8CY6PswKcfWyxKG47DWSpwfVEBvB4MartMMytQMArLioxQXtm1wsLCDj/zzDPKxx9/jEAgAI7jkJuba3AcdzY8PDzhwoULFlEU8cknn+Cpp57Cvffeizp16lwj/gwGg5g2YzaqNv8KjtjQDiO+vAM4uaWnmwx/FJUwevzbN2iMMVEUxTcyMjIsl86fx5OPPIIpheJMq6Ig2+MBMcbruj7FMIwvSiHhDwaDfCAQKAqsnpOTA8MwvKWkLVl2L0VRXpYkqX1sbGwQADIzM2Wr1brT7/d/bAU+nSfLyick44GgjjBHfYBM5HtOoK9gFweZvujVuv4+gKGVbDOTJOkxm832mNPptPn9fsuyZcswdOhQJCUlgTEGwzD4w4cP1161ahXatWuH119/Hc899xwmTpxY6q0B586dgyBFwx7Tq8yyrc4WUOwNHGb+/iOMsa70p219hXHDzJZleU58fHzb/PR0xO7cifWiiJaFCvs9wSBeAbBVECQXMI8xdpSIdhbPT0SXw8LCVg0ePLjvCy+8YD1z5gxefPFFTdf1HoIgBAGYkiQd8/v9bwL4kQpv/RFFcYrVap0+ZMgQa6F1pgUoGCH79+/vsWLFii6JgSAbb8iIaPoG6iQMAycUCNWi1TzsPbcAecdmShr0QYyxWKqE6FGW5ScTEhJmLFiwwMpxHMaOHYv4+Phrjpk8z6N58+Zo3LgxPvvsMwwZMgS9evUKeT3E6dOnYY0ZVKY27CqciXchzptS+4Qe2MUYa0kFm70K40btxp08z6dXj4623Jefj5dCXET2XDCID3QdQUE4GgwGG5dCU7LZbLMkSRpqGEZSTEyMNmDAAEvVqlVhmiZOnjyJ9evXu9PT0/NVVe3BGGths9m+mjJlirUs68z5n3+Dut22wBJWummvJ3szzu0YAs3wP0FE71aoAwCEh4enrV+/Pv5quI7Dhw+jT58+eO650kPIeDweTJs2DS+//HJRuMqSWLnyVxy60A5xDV4st/ys0x+h3/GXEDACwR91/a0A0YvA32xdyhi7t3r16kT5+XihDMnWLEmCg+dhsVgaSZI0p+R7IlK9Xu8yr9dbZfTo0fyECRMstWrVgsViKYpPNmnSJMegQYOqiqK4XZblt+++++6QjAaAvfsOI7beUyEZDQD26G4Ir3o7wITQcTJLAREJdvuf7mB2ux26HlrkrWkaLBZLSEYDQFRUJDTPvgqVr17ZhXrQ8ZwkyTzwKCsIp11h3IjLLmRZbiuZpvIwcJ07aXHwjOEhnkezBg0gSdIjjLGuJdNYLJbPR44caa1dO/SRt2vXrlyjRo0iJUmqWloY6qtQVRXJyQcQWX1MuY2JrD0BHCeXfvdyKWCM1TUMY/eDDz6oZmZmIjs7Gw8//DCaNWsWMk9Zd21eRYsWLeDJ2gLVf6nMdIaWj5z05WjDMTTheVgLGH1VmPG3ueyCMcYbfj9K3gheGuoA0L1e9OvXT7ZYLM8wxm52Op2n7Hb7FZvNtl0UxaSyOuwq4uLihIYNG4pldaDX64Ug2iDKseXSU8IawzR8YaycxZIx1s9qte62WCzJCQkJXY8dO8YSExORlJSEQCCAAQMGhMxrt9tBRMjICL3pt1gs6NCxPdIPPgTTLP2ibiIDWSkTUbNaNZwttLbhrzVN+vtuElBV9ahf143LRGVHhkeBt6Zks6Ft27ZsxYoVN9lstp7ffvuttXHjxhg2bFh7u93OVWQEWCyWInPjUBAEAYYeLDhTlrPhMY0AwHgWHmb7jTE2nEq531OSpEk2m23W7bffbm3evDkEQVAMw8DTTz+N559/vtxYpzzPo3bt2tiyZQvuvPPOkOkGD+qPy5e/xqnN3RHf+LUi6xQigi93F/LOvII61XRoVzjEcBzOmibyC+7LrlTc8RtitmEYC9Jyc1+eB/CPimLIjiUifKBpiOd5KIoCRVEwduxY6y23FAQ4bNu2LXfpUtnT11U4nU4cOnSozDR2ux12ux3enG2wR3cpM21++nJ06twDdWsn9Pjpp59W8YL9AOMt9UAUNLS8XwEzzWazzZoyZYo1IuJPm3ye52GVJPyxcyd69e1bbr2vXLmCkydPolmzZqXehAAUTPfRNgG8Nxm+3XfiLJNhscQiT70CjlPRr18PBPNyEbh0CbfwPKYEgxoPLKQKWNRcU05lEheDlyCYl6ggEm8ofKrr0MLCcODAAQSDQZimyYr7UEdGRiI/v7wrNgvQuHFjXLx4EdnZoS99ZYyhe7d2yDn9Jso6ZZCpw5v2AZ59eiI+/fRT2ev1to+pM3FstRYf9Exs9s7NYXG3vCbLlsV33nnnNYy+ClVVsW3DBrjd7lKo/4mTJ08iOzsbTZo0wcKFC7F27drrPEDS0tLw9aefInD4MLbLMlJlwnbBh8XaGWxl+WgbyMO531eh9/bt+I0IX+m6+Zmmub3Aa+V02XWo9MhmjEWCyYc5wS7HtpyPx/64CyfNIB4XRcQWTscZpok5moH3NRUTn3gCP//8MzZu3AhVVQMfffQR37dvX6Vx48Y4e/asvnv3bn7QoEGsvKnc5XKBiNSffvrJeOCBB5RQ6du1a4dfV71BaUeeo6qNX+NKzjpkakhLHosmDavglltuKbhNjxMQ1+BF/qpptCW8qe3i7m1o2rR0mwq7JGGEaeLTuXNx3/jx191QABQcAb9asADde/ZE//79kZGRgXnz5umrVq0yk5KSAhaLhWVnZ7O83FzbYABfyTJTCut6Nbz316qKI6aJOYaBAIA2Pp/7jGm6fEAfIqrYlFgMlXbZ5cXwVbzovKlqoxlMzV+PulXTUTXSht9++w1xhgGChAtEiKh6Ozz+o7h9YHP4/X5s3rzZyMrKmq3r+vawsLD3NU2LlCRpdzAYTOjQoUMDi8UCh8OBtm3blro2f//99+qePXvmcxzXvmHDho2HDRtmKRlSw+v14vvvv/cfO3YsRTclUZCia8bWnWSzRnRggAl35jojK/U9nWNB4dv/fM4nJCTgiSefw4WcBohtNLeITu6F7+BQP8TYB0aW2gfLvvsOnfbvRwRjmKHrqF2nDpq2bQtFUZCbm4v9mzfDm5uLPMPA1FdegcViwZo1a/Q1a9ZcMU3zR13X3QD2AcgE4LICvzfhOPtESZITGMMFKrheOlUQWEJCAjIvXjQ1XT+eT/QsgJVEVL7ZTimorCnxaMZZjnO8VW7U7zSOrY7H5YxLiIiIQF5eHr777js8//I8JLbbAF4MR+7FH8DnvYlOHZpjyZIleiAQqE4l7tS22Wz/iYyMvHvUqFHYt28fTp06hUceeaSI4USEDRs2GKtWrcpUVbU5AK/FYvnUMIw7mjdvTrVq1VKICKdPn/YnJydzgiD8EAgEHkaB0WAvXgx/EkBjgJlExm5Td78DoHZ4uPM/olKNs0QNRlTt5665geDK+W/gND/D/fcNL7UvMjIy8Mnbb2OZIGAMY4DTCa+qIuj3o4tp4iEifEOEyy1bomf//li7dq22a9curk+fPlqXLl0sn3/+uTczM3Ohy+V6jDGWoCjKgTZt2jjzL14ULl24YOqm6e4+cKB9yZIlvCiKuHDhAmrXrq1qmhZFpdxa+DfJxrm7HTG9OF27Ao6XwRgHo9A2zOl0om7duhAlR5FjvBLeHBmpOcjMzIRhGD+VZDRjrE5YWNjtBw4cQFRUFIgIQ4YMwfr169GxY8ciCVp2dnaWqqo9iSirMOsoxtiT+/btG3PkyJFmRARVVZNN01ygaVpWsSLWFf5Kdg4f0KM9NTv8Uaq0Q7JWR9rxtJC7+ri4ONw0ZAhu/f13PDllCl58+WUAwFuvv45lr72Gz30+bJMkMzwryztz5kyBMXbslltuabRs2TILAIwbN86WlJR0P2Nsjt1uf3bcuHHO2bNnCwBw4cIFrk6dOmF33HEHEwslk9WqVYPdbtdzc3OjcAPOAVdRqQ0axyvVJVstEaYOxhiia9yD224fjiNHjmDnzp144MHHYIn90yaOzCB4XsC2bdv8mqa9XgrJxFq1aqlX1zzGGLp27Yr169drs2bNcv3444870tLS7lVVtT6ViF9ORJmGYbzh8/nu9vv9dxuGMbvYx1Ae7LzkDDml2aI6wa9yOHPmTEgCHbt0gQ7g3vvuK3o28t57sU/T8KuuH8j2+Z5MTU19SNf1eEmS/ujRo0eRPbvT6US1atVUAHGyLNds27Zt0aC7ytjPPvvMr6oF5+7ffvsNgUBABXDNYKksKmdKbKoujrdR0JsK1X8JMfXfxJnsFuja41YMvPUBmGHjEJH0Z6gvV8bPcNgtZBhGKhGVJhM8cfz4cSklJQVAgenSokWLvKqqPqyqarjP5+tERMuIqEJmuJXAZdV3Xgi1hDHGIaL6RCz96Tdc7fDSEBERgZ07/9Tv7N69G6IokgaMJqJ3ieg7Isr3er07FyxY4PUWOvfv27cPZ86cEQGc8Pl8W+fPn++7Gv5rzZo18Pl8vuTk5B3Vq1f3tmzZMn/o0KEuv98/qKRqs7Ko7Jo9Q7LWWO6I7WfnBDsSmrwSMr2he3F8XQOYWq5H1/XWRHSitHSyLN8niuKHbdu2VY8fPy54vd7fXS7XnVROzLC/ggKftbDTNdt/V8MRQrVIZOLS/nuhYA9G3zvqOod7l8uFL7/8MpiWliaNGTOG8TyPzz//HJqmzVNVdWKJ8jiHw/GVJEm3Va9eXUtJSRGDweAowzB+YozJYWFhv9rt9vZxcXH6sWPHOJ/PNwjAFgDNAYQDOEhEV8poT4XW7Moyuycn2M8nNH0nMSNlGuIbzUJk0vU7VlP34fTO2+DP+8Nt6L52VHCxelm0a6HAdjwDBbfy3lhUn0qAMe5Bi6PRnHrdt9iuqkBLwnV5Dc7tviPIcaQnJSVRgwYN7BzH4ezZs76UlBSO5/lvgsHguwDuAxAB4FsquKy9lPIYQ8ENwVUAHCluOMEKnBtaoYCxyVQiMH/5bfmboiUxxnpwvG1lQrM51oxjs2Bx1ENUjQehhDeBaQSRn74cWanvwzT8e8jwdaYSdz3/X0HB6HZ8J9vqDEhq9ZmteIgt0wgi9+IiXDz4pI8M3y0AdgMYyvN8c47jBE3TjqPgdqO8/6Xq3xBuVJ/dh3GWJZbwZpw1rJnDl7cXWiAdZAbJNFWNDO+jRDT/76z4fwOMMY5x8lOM8c/I9jq84mwlmrrXdF1exTHGJxta3mNUcIHN/2n8LdM4XeskIAO4nRcj7gNjUSDKMrTcBQCW/V8dzaFQqBfuC6AGCu683kpE19/79H8U/xNOAkEUeEsuKi/t/3UU7nLLvcDl/3VUZmTnAThw9T8V3KW9sbS0ZbzrjlJ0r5VN/9+k9f+TdrQgImcI+kW44XCW/+L/PfzlCIf/4v8d/MvsfxD+ZfY/CP8y+x+Ef5n9D8K/zP4H4V9m/4PwL7P/Qfj/AAnwcV6GdGBJAAAAAElFTkSuQmCC", + "image/png": 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\n", 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" ] @@ -605,12 +590,12 @@ ")\n", "printsummary(out)\n", "out.write(\"water-acetonitrile-4.xyz\")\n", - "show(out)" + "plot_molecule(out);" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "id": "7db994f2-b5b1-4d96-bcec-db01c9798d3e", "metadata": {}, "outputs": [ @@ -625,12 +610,14 @@ }, { "data": { - "image/png": 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", + "image/png": 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\n", "text/plain": [ - "
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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -639,7 +626,7 @@ "print(\"Note: This density is meant to be equilibrated with NPT MD. It can be very inaccurate!\")\n", "out = packmol([water, acetonitrile], mole_fractions=[x_water, x_acetonitrile], n_atoms=100)\n", "print(f\"Guessed density: {out.get_density():.2f} kg/m^3\")\n", - "plot_molecule(out)" + "plot_molecule(out);" ] }, { @@ -654,7 +641,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "id": "bf26c1e9-173b-49fc-aad0-26061d95437f", "metadata": {}, "outputs": [ @@ -671,7 +658,7 @@ }, { "data": { - "image/png": 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", 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\n", 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" ] @@ -689,12 +676,12 @@ "print(f\"Radius of sphere: {details['radius']:.3f} ang.\")\n", "print(f\"Center of mass xyz (ang): {out.get_center_of_mass()}\")\n", "out.write(\"water-sphere.xyz\")\n", - "show(out)" + "plot_molecule(out);" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "id": "8107e15c-c644-4a3b-81c5-343d8d144ca0", "metadata": {}, "outputs": [ @@ -709,7 +696,7 @@ }, { "data": { - "image/png": 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", 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\n", "text/plain": [ "
" ] @@ -722,7 +709,8 @@ ], "source": [ "print(\n", - " \"2-1 water-acetonitrile in a sphere from exact density (in g/cm^3) and approximate number of atoms and mole fractions\"\n", + " \"2-1 water-acetonitrile in a sphere from exact density (in g/cm^3) and \"\n", + " \"approximate number of atoms and mole fractions\"\n", ")\n", "out, details = packmol(\n", " molecules=[water, acetonitrile],\n", @@ -734,7 +722,7 @@ ")\n", "printsummary(out, details)\n", "out.write(\"water-acetonitrile-sphere.xyz\")\n", - "show(out)" + "plot_molecule(out);" ] }, { @@ -751,7 +739,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "id": "2e1118ff-eb3d-4d5e-a667-532e65715c4c", "metadata": {}, "outputs": [ @@ -762,14 +750,14 @@ "3 water molecules, 3 ammonium, 1 chloride (non-periodic)\n", "Initial charges:\n", "Water: 0\n", - "Ammonia: 1\n", + "Ammonium: 1\n", "Chloride: -1\n", "Total charge of packmol-generated system: 2\n" ] }, { "data": { - "image/png": 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", + "image/png": 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x/vvvwZd/ZYK3/YAou5FdUAgS7DclffMBJDSAU19fv3ThwoXnX3PNNY32XmNmlJSU6AC+bvi/w9aCAfuMOLDUZebdRDRo9erV4zds2HCeaZphXdf/BmCVIAnloZrPXO7OIw8bLwtO1NTsafGc3+83mDmcoMT169evt5WUlCCr60hIii/B2xqTVVBor6t461IA/9eqCJBgjjdNs6ikpCReXFz8/TFmxuOPP25WVFT4AXzQWgGt4KNPPvlE9vsb9/yYGXPnzg3v2bPntSbHDWZeEAwGr9M07XZmXsnMbMVDj/s3/Tkhw9xdL8XceUWor69vdNzv9+PVV1+1ACS0rp2ZK2VZXvjcc8/FIXVO5JYWkW1dwGz6Wh0BEjSemffoun7OpEmTqk866aT6yy+/PNy9e/fQI4888l0oFDqbmZMfymslzFwtCMLsESNGhJcvXw5mRmVlJW6++ebYsmXLqpj5bwnG9EqoZnF4z47XDlveq75TIPsuwOnDR+Jf//oXtm7digULFmDo0KFhZn6SmRPeky4QCEwuKyvbrOutXxnNHAdAbdprLdl+vAhgFIB8AN8CWMbJRJAiGrpzt9hstumxWKwTAEEQJCsS0USQaAqiupvNyBxm4y98iH3fiegkEh1f5v/4KVdOj0mHzARGZDc2fnpGVLWFtHjcYFmWN9fV1T3GzMWHuu8g6Y5Ts059td+IzxNrzjchUPUetq/+VbnHRbXRaLSvoihVgUBgFjPPbzqecVANHeBbSiCiC0hQixQ139G5z51uX95FEGUf2IohUv8N/Fue0+sq/kEkKAuseP31fJAVOkQ0QBDVRXbPIFdunzvd3hPGgYQfRhej4c3wb34We7b9NcawnmJTm9rWHzsR2Umw+wecs9plcya/V/HmJWMN0VhtzZs3zzZ8+HCsW7cO99xzT3jTpk3vB4PBXySi76g0ngT5GkF0zuk17O8Od+cRB70uHtuLHV9drwf9n66z4sERB2uENWwRfomkeF6RZUX0ZPeDINhgRGsR1XbhjOHD8OWXi7+qr68/JSX6iSSQXOzrNGismjVUsGVfCE+XMdhXoB6amLYd6z/6Mb7buBa9e//wo9F1Hf369Qvv3LnzAmZefFgNR5vxRHSeIHnf7jfic4fd3e+w1zNb2L76V5FA1btLrHhwzMHaI0TUKycnp2zlypXOnTt3IhKJwOfzYfDgwYjFYujcuXMsFou1+cMEROR2u92Le/bs2ff66693mqaJ5+e+jNqgDycMeQuCePAvWTFbKF92iXGCbwd9+21Zsx7ZjBkz+OGHH54fDAavO5yOo+oTo0REguSe1/PUlxMyfd89Arqf8hf7+o9+PCwWD54D4D8HuTSsaZqUl5eHXr16NTqxa9cuSJKUksWcLpdr1rhx4wa+/PLL3784uvXWWzF+wmUo3fQocvv/ocX72Ipjx39vjoZqFtecc8nk3JauycrKIkmSEtou9mjbbeosUfZlu3PHJHUTCTJy+97tFCTPPUQ0gIiym17DzLsVRfm6qKioWRE4a9Ysg4hea3o8WYhIMQzjyhkzZnxvOgCIooiZf3oEgR1FaFoCM5sIVJZg4+KzwnW73l5lmeGriouLY6bZvA336quvhurq6t5LRMtRleMFyTslt88dzmRfhVtmBKZeRrIYG5Xd+YRVe/fulXw+38eBQOBaZq7af10gELjurrvuWrJ161bHFVdcIWmahjlz5kSLi4trNE1Lxd48HlEUhW7dujU70a9fP+haLTYvHRdy+oaoJNiFeLQ6trfirTjYKDeNwOMAXgcQ1zStdNKkSac+88wz9qysLGiahsceeyy+evXqvQASWo10VNXxouz29x+5vJPNlVxLuOrrQgwZxHhh3jPIy8tDfX09HnroIeP555+vCIVCg/iAbdaIqJfL5bpHEIQLiciIRCKvRKPRPzNzbVv1E5HkcDhqS0tLPQMGDGh0buXKlRg1apQ/FApNBZAPCHbA2gtgETdZtkVELo/H85dYLDY+Pz8/umvXLpssy0sDgcBVnOAuoUeV8YJoD5903mZVsiW+aYYeWIuasouwq2ILFKXxRh0jRowILV68+FZm/muqtR4MVVUfPu200+5cuHChun9CZTAYxKhRo7Q1a9bcH41GZyUaV8Nr6J4AKpl5VzI6jqqinkiKWqaW1Ldagv5FuHjCRc1MB4DJkye7ysrKxgNoN+N1XX+wtLR0QF5e3gWFhYViPB7HG2+8YVqWtSAWiz2ZTFwNpVCrSqKjy3hB3qbV/TdLUROfhyAINgRDLX+qNRwOw7KstH/H9UB43waOlxLR4GefffZ8ABaAfzPzxvbUcUQX9Q0TEC/Oysq6mYg6aZpWTfZBp/cf+WXCQ50xvQLblgzFls3f4sBXuYZhYNCgQaFNmzb9gpnfT4f+I5kjtjtHRKLb7S4eOHDgS7Nnzz67uLj4R7fddtsIin3rqt2eeM9KcXRDVo9bMOz0M1BSUoK6ujosXboUo0eP1qqrq5cB+DB9T3HkcsTmeCK6bODAgS+WlpY67fYfJsp88MEHuPiSX3DvkVvocHPh91O77SXs/Pr2uMdt36Trek+73V6jadrseDz+JB8De+e2hiPW+Ozs7M/mzJlz1sSJE5ud69u3b3xHlRjve9andkk59FzHPduKsHPNXbBM/UpmfjVdeo82juSivlNBQUGL53r27KnFwuWfrvuwv75r3f3xmLaj0Xm2DNRVvIXvFv8cO8vutixTvyNjehNStfoy1cHlcs279957DW5COBxmp9OpA+gFYJAgOueRYNdsrv51zuwz6hy+IXWC5NEFybsJwO8BqB39LEdi6HABBxUG9FdVNbxo0aLvTdc0jSdOnKh7vd5/NbnWBeAMABcAOBtAn47Wf6SHI66Ob+jCnedwOMaapnkCEZ3Tu3dv6t69O7744gtFFMWPA4HA5dwBX3U4ljiijCcij9vt/iQvL6/fNddc49J13Zo7d64eDAa/CofDcwCsZObNHa3zmKCji5wDg9vt/utVV10VMU2zUfF++umnhyVJuruj9R1L4YjJ8UTkstls/u3bt9ubLpZYunQpzjvvvF319fXN32dmaBVHkvG9OnXqVOb3+51Nz+m6Drfbbcbj8aPq3cKRTIf144lIIKLeRPRTIhoMQA+FQkJLHydctWoVnE5nUq8dMxyG9q5bAGSJwF1uoCIbCPchqutOFLADEbfDUX7ppZdG9+8wxcwcDAZ5yJAhIUEQbu3oevFYCu1a1BPRBXbg7xdKEt2pKOpwQfh+rdseZjwfi1l/kmV2Z2Vh8tVXC7quW0VFRVHDMN4KBoNXczuu2DnmSXFuPtnr9b4vSZIhSVLU4/G8A2BQw7lxHkBbqqqH3A9Pdzp5pChGbYKwk4j+AOBHHZ07jsWQsjqeiIaqqvrFQw89NMbv90tVVVXK9OnTL3Q4HMuJ6FwVeO0jVXWcfpivVtgFAR85HMoooiwncxanYNuPDC2Qql+Qz+db+uKLL3JTnnjiCcvjdm++RZYjyeyEucPpZDsQBuDq6NxxLIaU1PFE5JZluTYYDMo2W+PFJsFgED6fD2vtdgxM4hs1ADBW08ILTfNui3lum0VmaESqinqRiCBJzbvZiqIAzOib4OdGD+R6WXZmAdekQmCGxqTEeGauczgcm0pKSpqde/PNN3GCy2VKrdgPsJsggIHMBwjTQMoad4FA4M7Jkydr7777LizLgmmaKC4uxi233BLtEg636rvwcQC070+GFJOyIVBm/oCILi0sLHzaNM0CZoYsy5tDodDTlUQzmRnJLn1aZ1lgYGuqNGb4gXR8cJAAdAVgMXM1EZEH2PK2w9FzVAttgEMxOBwOfmNZhczcvA7J0CZSPlbf0IurZObq/f8HgcdnxmKJ7gwFAFhpmthqWVEcBV+0OBppl5c0DLyy2DTr58ViCQ251jLjEl3XDGAaJ7inS4bkaB/jmUNhYMSd0Wjd47GYGT9E9bLRsjA0HNb2MM+JMr/QHvqOR9r7JU13D/C2DAy4TVGUibIs5RIhwoxVloUnYrHQUtMkC5gWZZ7dbsKOQzpkIgYR/cQF3AXgghjgEgHDBmyrA54A8AYfsF49Q3o4YmbgZGhfjtiVNBnSS8b445SM8ccpGeOPUzLGH6dkjD9OyRh/nJIx/jjl/wHIRhkzuXD/igAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] @@ -786,13 +774,13 @@ "print(\"3 water molecules, 3 ammonium, 1 chloride (non-periodic)\")\n", "print(\"Initial charges:\")\n", "print(f\"Water: {water.properties.get('charge', 0)}\")\n", - "print(f\"Ammonia: {ammonium.properties.get('charge', 0)}\")\n", + "print(f\"Ammonium: {ammonium.properties.get('charge', 0)}\")\n", "print(f\"Chloride: {chloride.properties.get('charge', 0)}\")\n", "out = packmol(molecules=[water, ammonium, chloride], n_molecules=[3, 3, 1], density=0.4, sphere=True)\n", "tot_charge = out.properties.get(\"charge\", 0)\n", "print(f\"Total charge of packmol-generated system: {tot_charge}\")\n", "out.write(\"water-ammonium-chloride.xyz\")\n", - "show(out)" + "plot_molecule(out);" ] }, { @@ -806,7 +794,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "id": "e025d5a2-1b53-41f5-a1c8-37a12af7b904", "metadata": {}, "outputs": [ @@ -819,7 +807,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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\n", 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" ] @@ -831,13 +819,15 @@ } ], "source": [ + "from scm.plams import packmol_microsolvation\n", + "\n", "out = packmol_microsolvation(solute=acetonitrile, solvent=water, density=1.5, threshold=4.0)\n", "# for microsolvation it's a good idea to have a higher density than normal to get enough solvent molecules\n", "print(f\"Microsolvated structure: {len(out)} atoms.\")\n", "out.write(\"acetonitrile-microsolvated.xyz\")\n", "\n", "figsize = (3, 3)\n", - "show(out, figsize=figsize)" + "plot_molecule(out, figsize=figsize);" ] }, { @@ -851,13 +841,13 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "id": "3796ad06-8d5b-43e1-83a9-9a6fe896b366", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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\n", "text/plain": [ "
" ] @@ -869,14 +859,17 @@ } ], "source": [ + "from scm.plams import plot_molecule, fromASE\n", + "from ase.build import fcc111\n", + "\n", "rotation = \"90x,0y,0z\" # sideview of slab\n", "slab = fromASE(fcc111(\"Al\", size=(4, 6, 3), vacuum=15.0, orthogonal=True, periodic=True))\n", - "show(slab, figsize=figsize, rotation=rotation)" + "plot_molecule(slab, figsize=figsize, rotation=rotation);" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "id": "a4496b10-e9fb-41d1-83f7-9ba94a26bb96", "metadata": {}, "outputs": [ @@ -885,12 +878,12 @@ "output_type": "stream", "text": [ "water surrounding an Al slab, from an approximate density\n", - "534 atoms, density = 1.325 g/cm^3, box = 11.455, 14.881, 34.677, formula = Al72H308O154\n" + "606 atoms, density = 1.447 g/cm^3, box = 11.455, 14.881, 34.677, formula = Al72H356O178\n" ] }, { "data": { - "image/png": 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", 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\n", 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" ] @@ -903,15 +896,15 @@ ], "source": [ "print(\"water surrounding an Al slab, from an approximate density\")\n", - "out = packmol_on_slab(slab, water, density=1.0)\n", + "out = packmol_around(slab, water, density=1.0)\n", "printsummary(out)\n", "out.write(\"al-water-pure.xyz\")\n", - "show(out, figsize=figsize, rotation=rotation)" + "plot_molecule(out, figsize=figsize, rotation=rotation);" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "id": "2a6a9aa9-0f04-4be0-b46a-9204580d6cd5", "metadata": {}, "outputs": [ @@ -920,12 +913,12 @@ "output_type": "stream", "text": [ "2-1 water-acetonitrile mixture surrounding an Al slab, from mole fractions and an approximate density\n", - "468 atoms, density = 1.260 g/cm^3, box = 11.455, 14.881, 34.677, formula = C66H231Al72N33O66\n" + "528 atoms, density = 1.369 g/cm^3, box = 11.455, 14.881, 34.677, formula = C76H266Al72N38O76\n" ] }, { "data": { - "image/png": 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", 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\n", 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" ] @@ -938,10 +931,50 @@ ], "source": [ "print(\"2-1 water-acetonitrile mixture surrounding an Al slab, from mole fractions and an approximate density\")\n", - "out = packmol_on_slab(slab, [water, acetonitrile], mole_fractions=[x_water, x_acetonitrile], density=density)\n", + "out = packmol_around(slab, [water, acetonitrile], mole_fractions=[x_water, x_acetonitrile], density=density)\n", "printsummary(out)\n", "out.write(\"al-water-acetonitrile.xyz\")\n", - "show(out, figsize=figsize, rotation=rotation)" + "plot_molecule(out, figsize=figsize, rotation=rotation);" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "ca6746f7-47c9-4ec8-bfea-e4f8dfee49b4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "water surrounding non-orthorhombic Au(211) slab, from an exact number of molecules\n", + "out.lattice=[(9.1231573482, 0.0, 0.0), (3.6492629392999993, 4.4694160692, 0.0), (0.0, 0.0, 31.161091638)]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from ase.build import surface\n", + "\n", + "print(\"water surrounding non-orthorhombic Au(211) slab, from an exact number of molecules\")\n", + "slab = surface(\"Au\", (2, 1, 1), 6)\n", + "slab.center(vacuum=11.0, axis=2)\n", + "slab.set_pbc(True)\n", + "out = packmol_around(fromASE(slab), [water], n_molecules=[32], tolerance=1.8)\n", + "out.write(\"Au211-water.xyz\")\n", + "plot_molecule(out, figsize=figsize, rotation=rotation)\n", + "print(f\"{out.lattice=}\")" ] }, { @@ -951,18 +984,18 @@ "source": [ "## Pack inside voids in crystals\n", "\n", - "Use the ``packmol_in_void`` function. You can decrease ``tolerance`` if you need to pack very tightly. The default value for ``tolerance`` is 2.0." + "Use the ``packmol_around`` function. You can decrease ``tolerance`` if you need to pack very tightly. The default value for ``tolerance`` is 2.0." ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 26, "id": "0d030185-5a7a-4d5f-b348-9553a35e435e", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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\n", 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" ] @@ -974,14 +1007,17 @@ } ], "source": [ + "from scm.plams import fromASE\n", + "from ase.build import bulk\n", + "\n", "bulk_Al = fromASE(bulk(\"Al\", cubic=True).repeat((3, 3, 3)))\n", - "rotation = \"90x,5y,5z\"\n", - "show(bulk_Al, rotation=rotation, radii=0.4)" + "rotation = \"-85x,5y,0z\"\n", + "plot_molecule(bulk_Al, rotation=rotation, radii=0.4);" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 27, "id": "9bf8f26d-faf7-4283-8e5d-aa89725e3831", "metadata": {}, "outputs": [ @@ -994,7 +1030,7 @@ }, { "data": { - "image/png": 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", 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\n", 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" ] @@ -1006,13 +1042,13 @@ } ], "source": [ - "out = packmol_in_void(\n", - " host=bulk_Al,\n", + "out = packmol_around(\n", + " current=bulk_Al,\n", " molecules=[from_smiles(\"[H]\"), from_smiles(\"[He]\")],\n", " n_molecules=[50, 20],\n", " tolerance=1.5,\n", ")\n", - "show(out, rotation=rotation, radii=0.4)\n", + "plot_molecule(out, rotation=rotation, radii=0.4)\n", "printsummary(out)\n", "out.write(\"al-bulk-with-h-he.xyz\")" ] @@ -1031,7 +1067,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 28, "id": "22c8adb7-1512-4cef-8190-8eff2cb018c2", "metadata": {}, "outputs": [], @@ -1052,7 +1088,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 29, "id": "8bf26a15-e2ea-4b2a-add1-cb439c3652dc", "metadata": {}, "outputs": [ @@ -1060,7 +1096,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "system\n", + "System\n", " Atoms\n", " O 3.0728760000 3.9143770000 1.9903040000 region=mol0,oxygen_atom\n", " H 3.9160850000 3.5184940000 1.6850930000 mass=2.014 region=mol0\n", @@ -1088,6 +1124,8 @@ } ], "source": [ + "from scm.plams import AMSJob\n", + "\n", "out = packmol([water, n2], n_molecules=[2, 1], density=0.5)\n", "print(AMSJob(molecule=out).get_input())" ] @@ -1102,7 +1140,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 30, "id": "c174a783-7b5c-4cc0-b494-d56c9f9c6cf8", "metadata": {}, "outputs": [ @@ -1110,7 +1148,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "system\n", + "System\n", " Atoms\n", " O 3.0728760000 3.9143770000 1.9903040000 region=oxygen_atom,water\n", " H 3.9160850000 3.5184940000 1.6850930000 mass=2.014 region=water\n", @@ -1157,7 +1195,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 31, "id": "0ff63cd1-66fc-4205-9792-2eee2dbdb0c4", "metadata": {}, "outputs": [ @@ -1165,7 +1203,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "system\n", + "System\n", " Atoms\n", " O 3.0728760000 3.9143770000 1.9903040000 region=mol0\n", " H 3.9160850000 3.5184940000 1.6850930000 region=mol0\n", @@ -1207,7 +1245,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 32, "id": "6938fd3c-a30b-4036-8cee-ec10eef28cde", "metadata": {}, "outputs": [ @@ -1215,7 +1253,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "system\n", + "System\n", " Atoms\n", " O 3.0728760000 3.9143770000 1.9903040000 region=water\n", " H 3.9160850000 3.5184940000 1.6850930000 region=water\n", @@ -1252,7 +1290,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, diff --git a/doc/source/examples/PackMolExample/PackMol.rst.include b/doc/source/examples/PackMolExample/PackMol.rst.include index 5fd2802c..a38d51bb 100644 --- a/doc/source/examples/PackMolExample/PackMol.rst.include +++ b/doc/source/examples/PackMolExample/PackMol.rst.include @@ -1,18 +1,16 @@ -Worked Example --------------- - Initial imports -~~~~~~~~~~~~~~~ +--------------- .. code:: ipython3 - from scm.plams import * + from scm.plams import plot_molecule, from_smiles, Molecule + from scm.plams.interfaces.molecule.packmol import packmol, packmol_around from ase.visualize.plot import plot_atoms from ase.build import fcc111, bulk import matplotlib.pyplot as plt Helper functions -~~~~~~~~~~~~~~~~ +---------------- .. code:: ipython3 @@ -28,23 +26,16 @@ Helper functions if details: s += f'\n#added molecules per species: {details["n_molecules"]}, mole fractions: {details["mole_fractions"]}' print(s) - - - def show(mol, figsize=None, **kwargs): - """Show a molecule in a Jupyter notebook""" - plt.figure(figsize=figsize or (2, 2)) - plt.axis("off") - plot_atoms(toASE(mol), **kwargs) Liquid water (fluid with 1 component) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +------------------------------------- First, create the gasphase molecule: .. code:: ipython3 water = from_smiles("O") - show(water) + plot_molecule(water); @@ -53,13 +44,11 @@ First, create the gasphase molecule: .. code:: ipython3 - print( - "pure liquid from approximate number of atoms and exact density (in g/cm^3), cubic box with auto-determined size" - ) + print("pure liquid from approximate number of atoms and exact density (in g/cm^3), cubic box with auto-determined size") out = packmol(water, n_atoms=194, density=1.0) printsummary(out) out.write("water-1.xyz") - show(out) + plot_molecule(out); .. parsed-literal:: @@ -78,7 +67,7 @@ First, create the gasphase molecule: out = packmol(water, density=1.0, box_bounds=[0.0, 0.0, 0.0, 8.0, 12.0, 14.0]) printsummary(out) out.write("water-2.xyz") - show(out) + plot_molecule(out); .. parsed-literal:: @@ -97,7 +86,7 @@ First, create the gasphase molecule: out = packmol(water, n_molecules=64, density=1.0) printsummary(out) out.write("water-3.xyz") - show(out) + plot_molecule(out); .. parsed-literal:: @@ -116,7 +105,7 @@ First, create the gasphase molecule: out = packmol(water, n_molecules=64, box_bounds=[0.0, 0.0, 0.0, 12.0, 13.0, 14.0]) printsummary(out) out.write("water-4.xyz") - show(out) + plot_molecule(out); .. parsed-literal:: @@ -131,57 +120,38 @@ First, create the gasphase molecule: .. code:: ipython3 - print("water-5.xyz: pure liquid in non-orthorhombic box (requires AMS2022 or later)") - # first place the molecules in a cuboid surrounding the desired lattice - # then gradually change into the desired lattice using refine_lattice() - # note that the molecules may become distorted by this procedure - lattice = [[10.0, 2.0, -1.0], [-5.0, 8.0, 0.0], [0.0, -2.0, 11.0]] - temp_out = packmol( - water, - n_molecules=32, - box_bounds=[ - 0, - 0, - 0, - max(lattice[i][0] for i in range(3)) - min(lattice[i][0] for i in range(3)), - max(lattice[i][1] for i in range(3)) - min(lattice[i][1] for i in range(3)), - max(lattice[i][2] for i in range(3)) - min(lattice[i][2] for i in range(3)), - ], - ) - out = refine_lattice(temp_out, lattice=lattice) - if out is not None: - out.write("water-5.xyz") - print( - "Top: system in surrounding orthorhombic box before calling refine_lattice(). Bottom: System in non-orthorhombic box after calling refine_lattice()" - ) - show(temp_out) - show(out) + print("water-5.xyz: pure liquid in non-orthorhombic box (requires AMS2025 or later)") + # Non-orthorhombic boxes use UFF MD simulations behind the scenes + # You can pack inside any lattice using the packmol_around function + from scm.plams import init, Settings + + s = Settings() + s.log.stdout = 0 + init(config_settings=s) + box = Molecule() + box.lattice = [[10.0, 2.0, -1.0], [-5.0, 8.0, 0.0], [0.0, -2.0, 11.0]] + out = packmol_around(box, molecules=[water], n_molecules=[32]) + out.write("water-5.xyz") + plot_molecule(out); .. parsed-literal:: - water-5.xyz: pure liquid in non-orthorhombic box (requires AMS2022 or later) - PLAMS working folder: /home/robert/workspace/ams/main/scripting/scm/plams/doc/source/examples/PackMolExample/plams_workdir - Top: system in surrounding orthorhombic box before calling refine_lattice(). Bottom: System in non-orthorhombic box after calling refine_lattice() + water-5.xyz: pure liquid in non-orthorhombic box (requires AMS2025 or later) + PLAMS working folder: /path/plams_workdir.006 .. image:: PackMol_files/PackMol_10_1.png - -.. image:: PackMol_files/PackMol_10_2.png - - .. code:: ipython3 print("Experimental feature (AMS2025): guess density for pure liquid") - print( - "Note: This density is meant to be equilibrated with NPT MD. It can be very inaccurate!" - ) + print("Note: This density is meant to be equilibrated with NPT MD. It can be very inaccurate!") out = packmol(water, n_atoms=100) print(f"Guessed density: {out.get_density():.2f} kg/m^3") - plot_molecule(out) + plot_molecule(out); .. parsed-literal:: @@ -196,18 +166,26 @@ First, create the gasphase molecule: Water-acetonitrile mixture (fluid with 2 or more components) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +------------------------------------------------------------ Let’s also create a single acetonitrile molecule: .. code:: ipython3 acetonitrile = from_smiles("CC#N") - show(acetonitrile) + plot_molecule(acetonitrile) -.. image:: PackMol_files/PackMol_13_0.png + +.. parsed-literal:: + + + + + + +.. image:: PackMol_files/PackMol_13_1.png Set the desired mole fractions and density. Here, the density is @@ -220,9 +198,8 @@ density. # MIXTURES x_water = 0.666 # mole fraction x_acetonitrile = 1 - x_water # mole fraction - density = (x_water * 1.0 + x_acetonitrile * 0.76) / ( - x_water + x_acetonitrile - ) # weighted average of pure component densities + # weighted average of pure component densities + density = (x_water * 1.0 + x_acetonitrile * 0.76) / (x_water + x_acetonitrile) print("MIXTURES") print(f"x_water = {x_water:.3f}") @@ -245,7 +222,8 @@ mole fractions you put in. .. code:: ipython3 print( - "2-1 water-acetonitrile from approximate number of atoms and exact density (in g/cm^3), cubic box with auto-determined size" + "2-1 water-acetonitrile from approximate number of atoms and exact density (in g/cm^3), " + "cubic box with auto-determined size" ) out, details = packmol( molecules=[water, acetonitrile], @@ -256,7 +234,7 @@ mole fractions you put in. ) printsummary(out, details) out.write("water-acetonitrile-1.xyz") - show(out) + plot_molecule(out); .. parsed-literal:: @@ -302,7 +280,7 @@ The ``details`` is a dictionary as follows: ) printsummary(out, details) out.write("water-acetonitrile-2.xyz") - show(out) + plot_molecule(out); .. parsed-literal:: @@ -318,9 +296,7 @@ The ``details`` is a dictionary as follows: .. code:: ipython3 - print( - "2-1 water-acetonitrile from explicit number of molecules and density, cubic box with auto-determined size" - ) + print("2-1 water-acetonitrile from explicit number of molecules and density, cubic box with auto-determined size") out, details = packmol( molecules=[water, acetonitrile], n_molecules=[32, 16], @@ -329,7 +305,7 @@ The ``details`` is a dictionary as follows: ) printsummary(out, details) out.write("water-acetonitrile-3.xyz") - show(out) + plot_molecule(out); .. parsed-literal:: @@ -353,7 +329,7 @@ The ``details`` is a dictionary as follows: ) printsummary(out) out.write("water-acetonitrile-4.xyz") - show(out) + plot_molecule(out); .. parsed-literal:: @@ -369,14 +345,10 @@ The ``details`` is a dictionary as follows: .. code:: ipython3 print("Experimental feature (AMS2025): guess density for mixture") - print( - "Note: This density is meant to be equilibrated with NPT MD. It can be very inaccurate!" - ) - out = packmol( - [water, acetonitrile], mole_fractions=[x_water, x_acetonitrile], n_atoms=100 - ) + print("Note: This density is meant to be equilibrated with NPT MD. It can be very inaccurate!") + out = packmol([water, acetonitrile], mole_fractions=[x_water, x_acetonitrile], n_atoms=100) print(f"Guessed density: {out.get_density():.2f} kg/m^3") - plot_molecule(out) + plot_molecule(out); .. parsed-literal:: @@ -391,7 +363,7 @@ The ``details`` is a dictionary as follows: Pack inside sphere -~~~~~~~~~~~~~~~~~~ +------------------ Set ``sphere=True`` to pack in a sphere (non-periodic) instead of in a periodic box. The sphere will be centered near the origin. @@ -399,14 +371,12 @@ periodic box. The sphere will be centered near the origin. .. code:: ipython3 print("water in a sphere from exact density and number of molecules") - out, details = packmol( - molecules=[water], n_molecules=[100], density=1.0, return_details=True, sphere=True - ) + out, details = packmol(molecules=[water], n_molecules=[100], density=1.0, return_details=True, sphere=True) printsummary(out, details) print(f"Radius of sphere: {details['radius']:.3f} ang.") print(f"Center of mass xyz (ang): {out.get_center_of_mass()}") out.write("water-sphere.xyz") - show(out) + plot_molecule(out); .. parsed-literal:: @@ -425,7 +395,8 @@ periodic box. The sphere will be centered near the origin. .. code:: ipython3 print( - "2-1 water-acetonitrile in a sphere from exact density (in g/cm^3) and approximate number of atoms and mole fractions" + "2-1 water-acetonitrile in a sphere from exact density (in g/cm^3) and " + "approximate number of atoms and mole fractions" ) out, details = packmol( molecules=[water, acetonitrile], @@ -437,7 +408,7 @@ periodic box. The sphere will be centered near the origin. ) printsummary(out, details) out.write("water-acetonitrile-sphere.xyz") - show(out) + plot_molecule(out); .. parsed-literal:: @@ -452,7 +423,7 @@ periodic box. The sphere will be centered near the origin. Packing ions, total system charge -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +--------------------------------- The total system charge will be sum of the charges of the constituent molecules. @@ -466,15 +437,13 @@ In PLAMS, ``molecule.properties.charge`` specifies the charge: print("3 water molecules, 3 ammonium, 1 chloride (non-periodic)") print("Initial charges:") print(f"Water: {water.properties.get('charge', 0)}") - print(f"Ammonia: {ammonium.properties.get('charge', 0)}") + print(f"Ammonium: {ammonium.properties.get('charge', 0)}") print(f"Chloride: {chloride.properties.get('charge', 0)}") - out = packmol( - molecules=[water, ammonium, chloride], n_molecules=[3, 3, 1], density=0.4, sphere=True - ) + out = packmol(molecules=[water, ammonium, chloride], n_molecules=[3, 3, 1], density=0.4, sphere=True) tot_charge = out.properties.get("charge", 0) print(f"Total charge of packmol-generated system: {tot_charge}") out.write("water-ammonium-chloride.xyz") - show(out) + plot_molecule(out); .. parsed-literal:: @@ -482,7 +451,7 @@ In PLAMS, ``molecule.properties.charge`` specifies the charge: 3 water molecules, 3 ammonium, 1 chloride (non-periodic) Initial charges: Water: 0 - Ammonia: 1 + Ammonium: 1 Chloride: -1 Total charge of packmol-generated system: 2 @@ -492,22 +461,22 @@ In PLAMS, ``molecule.properties.charge`` specifies the charge: Microsolvation -~~~~~~~~~~~~~~ +-------------- ``packmol_microsolvation`` can create a microsolvation sphere around a solute. .. code:: ipython3 - out = packmol_microsolvation( - solute=acetonitrile, solvent=water, density=1.5, threshold=4.0 - ) + from scm.plams import packmol_microsolvation + + out = packmol_microsolvation(solute=acetonitrile, solvent=water, density=1.5, threshold=4.0) # for microsolvation it's a good idea to have a higher density than normal to get enough solvent molecules print(f"Microsolvated structure: {len(out)} atoms.") out.write("acetonitrile-microsolvated.xyz") figsize = (3, 3) - show(out, figsize=figsize) + plot_molecule(out, figsize=figsize); .. parsed-literal:: @@ -520,15 +489,18 @@ solute. Solid-liquid or solid-gas interfaces -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +------------------------------------ First, create a slab using the ASE ``fcc111`` function .. code:: ipython3 + from scm.plams import plot_molecule, fromASE + from ase.build import fcc111 + rotation = "90x,0y,0z" # sideview of slab slab = fromASE(fcc111("Al", size=(4, 6, 3), vacuum=15.0, orthogonal=True, periodic=True)) - show(slab, figsize=figsize, rotation=rotation) + plot_molecule(slab, figsize=figsize, rotation=rotation); @@ -538,16 +510,16 @@ First, create a slab using the ASE ``fcc111`` function .. code:: ipython3 print("water surrounding an Al slab, from an approximate density") - out = packmol_on_slab(slab, water, density=1.0) + out = packmol_around(slab, water, density=1.0) printsummary(out) out.write("al-water-pure.xyz") - show(out, figsize=figsize, rotation=rotation) + plot_molecule(out, figsize=figsize, rotation=rotation); .. parsed-literal:: water surrounding an Al slab, from an approximate density - 534 atoms, density = 1.325 g/cm^3, box = 11.455, 14.881, 34.677, formula = Al72H308O154 + 606 atoms, density = 1.447 g/cm^3, box = 11.455, 14.881, 34.677, formula = Al72H356O178 @@ -556,54 +528,77 @@ First, create a slab using the ASE ``fcc111`` function .. code:: ipython3 - print( - "2-1 water-acetonitrile mixture surrounding an Al slab, from mole fractions and an approximate density" - ) - out = packmol_on_slab( - slab, [water, acetonitrile], mole_fractions=[x_water, x_acetonitrile], density=density - ) + print("2-1 water-acetonitrile mixture surrounding an Al slab, from mole fractions and an approximate density") + out = packmol_around(slab, [water, acetonitrile], mole_fractions=[x_water, x_acetonitrile], density=density) printsummary(out) out.write("al-water-acetonitrile.xyz") - show(out, figsize=figsize, rotation=rotation) + plot_molecule(out, figsize=figsize, rotation=rotation); .. parsed-literal:: 2-1 water-acetonitrile mixture surrounding an Al slab, from mole fractions and an approximate density - 468 atoms, density = 1.260 g/cm^3, box = 11.455, 14.881, 34.677, formula = C66H231Al72N33O66 + 528 atoms, density = 1.369 g/cm^3, box = 11.455, 14.881, 34.677, formula = C76H266Al72N38O76 .. image:: PackMol_files/PackMol_34_1.png +.. code:: ipython3 + + from ase.build import surface + + print("water surrounding non-orthorhombic Au(211) slab, from an exact number of molecules") + slab = surface("Au", (2, 1, 1), 6) + slab.center(vacuum=11.0, axis=2) + slab.set_pbc(True) + out = packmol_around(fromASE(slab), [water], n_molecules=[32], tolerance=1.8) + out.write("Au211-water.xyz") + plot_molecule(out, figsize=figsize, rotation=rotation) + print(f"{out.lattice=}") + + +.. parsed-literal:: + + water surrounding non-orthorhombic Au(211) slab, from an exact number of molecules + out.lattice=[(9.1231573482, 0.0, 0.0), (3.6492629392999993, 4.4694160692, 0.0), (0.0, 0.0, 31.161091638)] + + + +.. image:: PackMol_files/PackMol_35_1.png + + Pack inside voids in crystals -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +----------------------------- -Use the ``packmol_in_void`` function. You can decrease ``tolerance`` if +Use the ``packmol_around`` function. You can decrease ``tolerance`` if you need to pack very tightly. The default value for ``tolerance`` is 2.0. .. code:: ipython3 + from scm.plams import fromASE + from ase.build import bulk + bulk_Al = fromASE(bulk("Al", cubic=True).repeat((3, 3, 3))) - rotation = "90x,5y,5z" - show(bulk_Al, rotation=rotation, radii=0.4) + rotation = "-85x,5y,0z" + plot_molecule(bulk_Al, rotation=rotation, radii=0.4); -.. image:: PackMol_files/PackMol_36_0.png +.. image:: PackMol_files/PackMol_37_0.png .. code:: ipython3 - out = packmol_in_void( - host=bulk_Al, + out = packmol_around( + current=bulk_Al, molecules=[from_smiles("[H]"), from_smiles("[He]")], n_molecules=[50, 20], tolerance=1.5, ) - show(out, rotation=rotation, radii=0.4) + plot_molecule(out, rotation=rotation, radii=0.4) printsummary(out) out.write("al-bulk-with-h-he.xyz") @@ -614,11 +609,11 @@ you need to pack very tightly. The default value for ``tolerance`` is -.. image:: PackMol_files/PackMol_37_1.png +.. image:: PackMol_files/PackMol_38_1.png Bonds, atom properties (force field types, regions, …) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +------------------------------------------------------ The ``packmol()`` function accepts the arguments ``keep_bonds`` and ``keep_atom_properties``. These options will keep the bonds defined for @@ -644,13 +639,15 @@ block for an AMS job: .. code:: ipython3 + from scm.plams import AMSJob + out = packmol([water, n2], n_molecules=[2, 1], density=0.5) print(AMSJob(molecule=out).get_input()) .. parsed-literal:: - system + System Atoms O 3.0728760000 3.9143770000 1.9903040000 region=mol0,oxygen_atom H 3.9160850000 3.5184940000 1.6850930000 mass=2.014 region=mol0 @@ -693,7 +690,7 @@ option lets you set custom names. .. parsed-literal:: - system + System Atoms O 3.0728760000 3.9143770000 1.9903040000 region=oxygen_atom,water H 3.9160850000 3.5184940000 1.6850930000 mass=2.014 region=water @@ -730,7 +727,7 @@ previous regions (in this example “oxygen_atom”) and mass. .. parsed-literal:: - system + System Atoms O 3.0728760000 3.9143770000 1.9903040000 region=mol0 H 3.9160850000 3.5184940000 1.6850930000 region=mol0 @@ -773,7 +770,7 @@ previous regions (in this example “oxygen_atom”) and mass. .. parsed-literal:: - system + System Atoms O 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@@ -11,7 +12,6 @@ # ## Helper functions - def printsummary(mol, details=None): if details: density = details["density"] @@ -26,87 +26,68 @@ def printsummary(mol, details=None): print(s) -def show(mol, figsize=None, **kwargs): - """Show a molecule in a Jupyter notebook""" - plt.figure(figsize=figsize or (2, 2)) - plt.axis("off") - plot_atoms(toASE(mol), **kwargs) - - # ## Liquid water (fluid with 1 component) # First, create the gasphase molecule: water = from_smiles("O") -show(water) +plot_molecule(water); print("pure liquid from approximate number of atoms and exact density (in g/cm^3), cubic box with auto-determined size") out = packmol(water, n_atoms=194, density=1.0) printsummary(out) out.write("water-1.xyz") -show(out) +plot_molecule(out); print("pure liquid from approximate density (in g/cm^3) and an orthorhombic box") out = packmol(water, density=1.0, box_bounds=[0.0, 0.0, 0.0, 8.0, 12.0, 14.0]) printsummary(out) out.write("water-2.xyz") -show(out) +plot_molecule(out); print("pure liquid with explicit number of molecules and exact density") out = packmol(water, n_molecules=64, density=1.0) printsummary(out) out.write("water-3.xyz") -show(out) +plot_molecule(out); print("pure liquid with explicit number of molecules and box") out = packmol(water, n_molecules=64, box_bounds=[0.0, 0.0, 0.0, 12.0, 13.0, 14.0]) printsummary(out) out.write("water-4.xyz") -show(out) - - -print("water-5.xyz: pure liquid in non-orthorhombic box (requires AMS2022 or later)") -# first place the molecules in a cuboid surrounding the desired lattice -# then gradually change into the desired lattice using refine_lattice() -# note that the molecules may become distorted by this procedure -lattice = [[10.0, 2.0, -1.0], [-5.0, 8.0, 0.0], [0.0, -2.0, 11.0]] -temp_out = packmol( - water, - n_molecules=32, - box_bounds=[ - 0, - 0, - 0, - max(lattice[i][0] for i in range(3)) - min(lattice[i][0] for i in range(3)), - max(lattice[i][1] for i in range(3)) - min(lattice[i][1] for i in range(3)), - max(lattice[i][2] for i in range(3)) - min(lattice[i][2] for i in range(3)), - ], -) -out = refine_lattice(temp_out, lattice=lattice) -if out is not None: - out.write("water-5.xyz") - print( - "Top: system in surrounding orthorhombic box before calling refine_lattice(). Bottom: System in non-orthorhombic box after calling refine_lattice()" - ) - show(temp_out) - show(out) +plot_molecule(out); + + +print("water-5.xyz: pure liquid in non-orthorhombic box (requires AMS2025 or later)") +# Non-orthorhombic boxes use UFF MD simulations behind the scenes +# You can pack inside any lattice using the packmol_around function +from scm.plams import init, Settings + +s = Settings() +s.log.stdout = 0 +init(config_settings=s) +box = Molecule() +box.lattice = [[10.0, 2.0, -1.0], [-5.0, 8.0, 0.0], [0.0, -2.0, 11.0]] +out = packmol_around(box, molecules=[water], n_molecules=[32]) +out.write("water-5.xyz") +plot_molecule(out); print("Experimental feature (AMS2025): guess density for pure liquid") print("Note: This density is meant to be equilibrated with NPT MD. It can be very inaccurate!") out = packmol(water, n_atoms=100) print(f"Guessed density: {out.get_density():.2f} kg/m^3") -plot_molecule(out) +plot_molecule(out); # ## Water-acetonitrile mixture (fluid with 2 or more components) # Let's also create a single acetonitrile molecule: acetonitrile = from_smiles("CC#N") -show(acetonitrile) +plot_molecule(acetonitrile) # Set the desired mole fractions and density. Here, the density is calculated as the weighted average of water (1.0 g/cm^3) and acetonitrile (0.76 g/cm^3) densities, but you could use any other density. @@ -114,9 +95,8 @@ def show(mol, figsize=None, **kwargs): # MIXTURES x_water = 0.666 # mole fraction x_acetonitrile = 1 - x_water # mole fraction -density = (x_water * 1.0 + x_acetonitrile * 0.76) / ( - x_water + x_acetonitrile -) # weighted average of pure component densities +# weighted average of pure component densities +density = (x_water * 1.0 + x_acetonitrile * 0.76) / (x_water + x_acetonitrile) print("MIXTURES") print(f"x_water = {x_water:.3f}") @@ -127,7 +107,8 @@ def show(mol, figsize=None, **kwargs): # By setting ``return_details=True``, you can get information about the mole fractions of the returned system. They may not exactly match the mole fractions you put in. print( - "2-1 water-acetonitrile from approximate number of atoms and exact density (in g/cm^3), cubic box with auto-determined size" + "2-1 water-acetonitrile from approximate number of atoms and exact density (in g/cm^3), " + "cubic box with auto-determined size" ) out, details = packmol( molecules=[water, acetonitrile], @@ -138,7 +119,7 @@ def show(mol, figsize=None, **kwargs): ) printsummary(out, details) out.write("water-acetonitrile-1.xyz") -show(out) +plot_molecule(out); # The ``details`` is a dictionary as follows: @@ -157,7 +138,7 @@ def show(mol, figsize=None, **kwargs): ) printsummary(out, details) out.write("water-acetonitrile-2.xyz") -show(out) +plot_molecule(out); print("2-1 water-acetonitrile from explicit number of molecules and density, cubic box with auto-determined size") @@ -169,7 +150,7 @@ def show(mol, figsize=None, **kwargs): ) printsummary(out, details) out.write("water-acetonitrile-3.xyz") -show(out) +plot_molecule(out); print("2-1 water-acetonitrile from explicit number of molecules and box") @@ -180,18 +161,18 @@ def show(mol, figsize=None, **kwargs): ) printsummary(out) out.write("water-acetonitrile-4.xyz") -show(out) +plot_molecule(out); print("Experimental feature (AMS2025): guess density for mixture") print("Note: This density is meant to be equilibrated with NPT MD. It can be very inaccurate!") out = packmol([water, acetonitrile], mole_fractions=[x_water, x_acetonitrile], n_atoms=100) print(f"Guessed density: {out.get_density():.2f} kg/m^3") -plot_molecule(out) +plot_molecule(out); # ## Pack inside sphere -# +# # Set ``sphere=True`` to pack in a sphere (non-periodic) instead of in a periodic box. The sphere will be centered near the origin. print("water in a sphere from exact density and number of molecules") @@ -200,11 +181,12 @@ def show(mol, figsize=None, **kwargs): print(f"Radius of sphere: {details['radius']:.3f} ang.") print(f"Center of mass xyz (ang): {out.get_center_of_mass()}") out.write("water-sphere.xyz") -show(out) +plot_molecule(out); print( - "2-1 water-acetonitrile in a sphere from exact density (in g/cm^3) and approximate number of atoms and mole fractions" + "2-1 water-acetonitrile in a sphere from exact density (in g/cm^3) and " + "approximate number of atoms and mole fractions" ) out, details = packmol( molecules=[water, acetonitrile], @@ -216,13 +198,13 @@ def show(mol, figsize=None, **kwargs): ) printsummary(out, details) out.write("water-acetonitrile-sphere.xyz") -show(out) +plot_molecule(out); # ## Packing ions, total system charge -# +# # The total system charge will be sum of the charges of the constituent molecules. -# +# # In PLAMS, ``molecule.properties.charge`` specifies the charge: ammonium = from_smiles("[NH4+]") # ammonia.properties.charge == +1 @@ -230,73 +212,93 @@ def show(mol, figsize=None, **kwargs): print("3 water molecules, 3 ammonium, 1 chloride (non-periodic)") print("Initial charges:") print(f"Water: {water.properties.get('charge', 0)}") -print(f"Ammonia: {ammonium.properties.get('charge', 0)}") +print(f"Ammonium: {ammonium.properties.get('charge', 0)}") print(f"Chloride: {chloride.properties.get('charge', 0)}") out = packmol(molecules=[water, ammonium, chloride], n_molecules=[3, 3, 1], density=0.4, sphere=True) tot_charge = out.properties.get("charge", 0) print(f"Total charge of packmol-generated system: {tot_charge}") out.write("water-ammonium-chloride.xyz") -show(out) +plot_molecule(out); # ## Microsolvation # ``packmol_microsolvation`` can create a microsolvation sphere around a solute. +from scm.plams import packmol_microsolvation + out = packmol_microsolvation(solute=acetonitrile, solvent=water, density=1.5, threshold=4.0) # for microsolvation it's a good idea to have a higher density than normal to get enough solvent molecules print(f"Microsolvated structure: {len(out)} atoms.") out.write("acetonitrile-microsolvated.xyz") figsize = (3, 3) -show(out, figsize=figsize) +plot_molecule(out, figsize=figsize); # ## Solid-liquid or solid-gas interfaces # First, create a slab using the ASE ``fcc111`` function +from scm.plams import plot_molecule, fromASE +from ase.build import fcc111 + rotation = "90x,0y,0z" # sideview of slab slab = fromASE(fcc111("Al", size=(4, 6, 3), vacuum=15.0, orthogonal=True, periodic=True)) -show(slab, figsize=figsize, rotation=rotation) +plot_molecule(slab, figsize=figsize, rotation=rotation); print("water surrounding an Al slab, from an approximate density") -out = packmol_on_slab(slab, water, density=1.0) +out = packmol_around(slab, water, density=1.0) printsummary(out) out.write("al-water-pure.xyz") -show(out, figsize=figsize, rotation=rotation) +plot_molecule(out, figsize=figsize, rotation=rotation); print("2-1 water-acetonitrile mixture surrounding an Al slab, from mole fractions and an approximate density") -out = packmol_on_slab(slab, [water, acetonitrile], mole_fractions=[x_water, x_acetonitrile], density=density) +out = packmol_around(slab, [water, acetonitrile], mole_fractions=[x_water, x_acetonitrile], density=density) printsummary(out) out.write("al-water-acetonitrile.xyz") -show(out, figsize=figsize, rotation=rotation) +plot_molecule(out, figsize=figsize, rotation=rotation); + + +from ase.build import surface + +print("water surrounding non-orthorhombic Au(211) slab, from an exact number of molecules") +slab = surface("Au", (2, 1, 1), 6) +slab.center(vacuum=11.0, axis=2) +slab.set_pbc(True) +out = packmol_around(fromASE(slab), [water], n_molecules=[32], tolerance=1.8) +out.write("Au211-water.xyz") +plot_molecule(out, figsize=figsize, rotation=rotation) +print(f"{out.lattice=}") # ## Pack inside voids in crystals -# -# Use the ``packmol_in_void`` function. You can decrease ``tolerance`` if you need to pack very tightly. The default value for ``tolerance`` is 2.0. +# +# Use the ``packmol_around`` function. You can decrease ``tolerance`` if you need to pack very tightly. The default value for ``tolerance`` is 2.0. + +from scm.plams import fromASE +from ase.build import bulk bulk_Al = fromASE(bulk("Al", cubic=True).repeat((3, 3, 3))) -rotation = "90x,5y,5z" -show(bulk_Al, rotation=rotation, radii=0.4) +rotation = "-85x,5y,0z" +plot_molecule(bulk_Al, rotation=rotation, radii=0.4); -out = packmol_in_void( - host=bulk_Al, +out = packmol_around( + current=bulk_Al, molecules=[from_smiles("[H]"), from_smiles("[He]")], n_molecules=[50, 20], tolerance=1.5, ) -show(out, rotation=rotation, radii=0.4) +plot_molecule(out, rotation=rotation, radii=0.4) printsummary(out) out.write("al-bulk-with-h-he.xyz") # ## Bonds, atom properties (force field types, regions, ...) -# +# # The ``packmol()`` function accepts the arguments ``keep_bonds`` and ``keep_atom_properties``. These options will keep the bonds defined for the constitutent molecules, as well as any atomic properties. -# +# # The bonds and atom properties are easiest to see by printing the System block for an AMS job: water = from_smiles("O") @@ -313,11 +315,13 @@ def show(mol, figsize=None, **kwargs): water.delete_bond(water[1, 2]) # delete bond between atoms 1 and 2 (O and H) +from scm.plams import AMSJob + out = packmol([water, n2], n_molecules=[2, 1], density=0.5) print(AMSJob(molecule=out).get_input()) -# By default, the ``packmol()`` function assigns regions called ``mol0``, ``mol1``, etc. to the different added molecules. The ``region_names`` option lets you set custom names. +# By default, the ``packmol()`` function assigns regions called ``mol0``, ``mol1``, etc. to the different added molecules. The ``region_names`` option lets you set custom names. out = packmol( [water, n2], @@ -328,7 +332,7 @@ def show(mol, figsize=None, **kwargs): print(AMSJob(molecule=out).get_input()) -# Below, we also set ``keep_atom_properties=False``, this will remove the previous regions (in this example "oxygen_atom") and mass. +# Below, we also set ``keep_atom_properties=False``, this will remove the previous regions (in this example "oxygen_atom") and mass. out = packmol([water, n2], n_molecules=[2, 1], density=0.5, keep_atom_properties=False) print(AMSJob(molecule=out).get_input()) @@ -345,3 +349,4 @@ def show(mol, figsize=None, **kwargs): keep_atom_properties=False, ) print(AMSJob(molecule=out).get_input()) + diff --git a/interfaces/molecule/packmol.py b/interfaces/molecule/packmol.py index 9d15e577..e47fa098 100644 --- a/interfaces/molecule/packmol.py +++ b/interfaces/molecule/packmol.py @@ -11,7 +11,7 @@ from scm.plams.tools.periodic_table import PeriodicTable from scm.plams.tools.units import Units from scm.plams.interfaces.molecule.rdkit import readpdb, writepdb -from scm.plams.core.functions import requires_optional_package, delete_job +from scm.plams.core.functions import requires_optional_package, log from scm.plams.core.settings import Settings if TYPE_CHECKING: @@ -104,6 +104,7 @@ def __init__( n_molecules = self._get_n_molecules_from_density_and_box_bounds(self.molecule, box_bounds, density) assert n_molecules is not None or n_atoms is not None if n_molecules is None: + assert n_atoms is not None self.n_molecules = self._get_n_molecules(self.molecule, n_atoms) else: self.n_molecules = n_molecules @@ -112,7 +113,9 @@ def __init__( self.fixed = False self.sphere = sphere - def _get_n_molecules_from_density_and_box_bounds(self, molecule: Molecule, box_bounds: List[float], density: float): + def _get_n_molecules_from_density_and_box_bounds( + self, molecule: Molecule, box_bounds: List[float], density: float + ) -> int: """density in g/cm^3""" molecule_mass = molecule.get_mass(unit="g") volume_ang3 = self.get_volume(box_bounds) @@ -766,11 +769,86 @@ def packmol_in_void( return ret +def _run_uff_md( + ucs: "ChemicalSystem", + nsteps: int = 1000, + vectors=None, + fixed_atoms: Optional[Sequence[int]] = None, + keepjob: bool = False, +) -> "ChemicalSystem": + """ + Runs UFF MD with SHAKE all bonds, keeps ``fixed_atoms`` (0-based atom indices) fixed, + if ``vectors`` is not None will transform into those vectors + + Returns: The final system from the MD simulation. + + Raises: PackmolError if something goes worng. + """ + from scm.plams.interfaces.adfsuite.quickjobs import _ensure_init + from scm.plams.core.functions import finish, delete_job + from scm.plams import config + + thermostatted_region = "PACKMOL_thermostatted" + md_ucs = ucs.copy() + md_ucs.set_atoms_in_region( + [x for x in range(len(md_ucs)) if fixed_atoms is None or x not in fixed_atoms], thermostatted_region + ) + + s = Settings() + s.input.ForceField.Type = "UFF" + s.input.ams.Task = "MolecularDynamics" + if fixed_atoms: + s.input.ams.Constraints.AtomList = " ".join(str(x + 1) for x in fixed_atoms) + s.input.ams.MolecularDynamics.NSteps = nsteps + s.input.ams.MolecularDynamics.TimeStep = 0.5 + s.input.ams.MolecularDynamics.Shake.All = "bonds * *" + s.input.ams.MolecularDynamics.InitialVelocities.Type = "Zero" + # s.input.ams.MolecularDynamics.InitialVelocities.Temperature = 10 + s.input.ams.MolecularDynamics.Thermostat.Temperature = 5 + s.input.ams.MolecularDynamics.Thermostat.Region = thermostatted_region + s.input.ams.MolecularDynamics.Thermostat.Tau = 2 + s.input.ams.MolecularDynamics.Thermostat.Type = "Berendsen" + + if vectors is not None: + x = vectors + target_lattice_str = f""" + {x[0][0]} {x[0][1]} {x[0][2]} + {x[1][0]} {x[1][1]} {x[1][2]} + {x[2][0]} {x[2][1]} {x[2][2]} + """ + + s.input.ams.MolecularDynamics.Deformation.StartStep = 1 + s.input.ams.MolecularDynamics.Deformation.StopStep = (nsteps * 3) // 4 + s.input.ams.MolecularDynamics.Deformation.TargetLattice._1 = target_lattice_str + + previous_config = config.copy() + config.job.pickle = False + config.log.stdout = 0 + # TODO: fix this so that it doesn't leave plams_workdir on disk if it is created + job = AMSJob(settings=s, molecule=md_ucs, name="shakemd") + job.run() + job.results.wait() + config.job.pickle = previous_config.job.pickle + config.log.stdout = previous_config.log.stdout + if not job.ok(): + raise PackMolError( + f"Try a lower density or a less skewed cell! Original file in {job.path} . " + + str(job.results.get_errormsg()) + ) + my_packed = job.results.get_main_system() + if not keepjob: + delete_job(job) + + my_packed.remove_region("PACKMOL_thermostatted") + return my_packed + + @requires_optional_package("scm.libbase") def packmol_around( current: Union[Molecule, "ChemicalSystem"], molecules: Union[Molecule, List[Molecule]], return_details: bool = False, + always_run_md: bool = False, **kwargs, ) -> Molecule: """Pack around the current molecule. @@ -778,7 +856,12 @@ def packmol_around( ``current``: Molecule Must have a 3D lattice - The ``current`` molecule will be mapped to [0..1]. The "box" will be set to the min/max of the components of each lattice vector. + ``always_run_md``: bool + If True, will run UFF MD also for orthorhombic cells. For nonorthorhombic cells, MD is always run irrespective of this flag. + + For all other arguments, see the ``packmol`` function. + + In the returned ``Molecule`, the system will be mapped to [0..1]. It has the same lattice has ``current``. """ from scm.libbase import ( UnifiedChemicalSystem as ChemicalSystem, @@ -786,15 +869,17 @@ def packmol_around( ) from scm.utils.conversions import plams_molecule_to_chemsys, chemsys_to_plams_molecule + loglevel = 7 + if isinstance(current, Molecule): original_ucs = plams_molecule_to_chemsys(current) else: original_ucs = current.copy() - assert isinstance(current, ChemicalSystem) + assert isinstance(original_ucs, ChemicalSystem) original_ucs.map_atoms(0) - # step 1: find min/max of lattice - if current.lattice.num_vectors != 3: + # step 1: store info about original system + if original_ucs.lattice.num_vectors != 3: raise ValueError(f"Input molecule `current` must have 3D lattice, got: {current.lattice}") original_frac_coords = original_ucs.get_fractional_coordinates() @@ -805,79 +890,101 @@ def packmol_around( current_atomic_volume = ( (4 / 3) * 3.14159 * np.sum(np.fromiter((at.element.radius for at in original_ucs), dtype=np.float32)) ) + current_atomic_volume /= 0.74 # use packing efficiency in ccp as example to take up more volume remaining_volume = original_volume - current_atomic_volume # temporary value to call the original packmol with - box_bounds_for_remaining_volume = [ - 0.0, - 0.0, - 0.0, - remaining_volume ** (1 / 3.0), - remaining_volume ** (1 / 3.0), - remaining_volume ** (1 / 3.0), - ] + temp_L = remaining_volume ** (1 / 3.0) + box_bounds_for_remaining_volume = [0.0, 0.0, 0.0, temp_L, temp_L, temp_L] # it is unnecessary to actually pack the molecules, this is just used to get the "details" + # details will contain the correct number of molecules to pack in the combined system + # TODO: reorganize the packmol function so that one can get this info without calling packmol + log(f"Initial packing to determine number of molecules: {molecules}, {box_bounds_for_remaining_volume}", loglevel) _, details = packmol(molecules=molecules, return_details=True, box_bounds=box_bounds_for_remaining_volume, **kwargs) - maxcomponents = np.max(current.lattice.vectors, axis=0) - np.min(current.lattice.vectors, axis=0) + # find cuboid parallel along x/y/z that is guaranteed to encompass the original lattice + maxcomponents = np.max(original_ucs.lattice.vectors, axis=0) - np.min(original_ucs.lattice.vectors, axis=0) box_bounds = [0.0, 0.0, 0.0] + list(maxcomponents) - will_run_uff_md = any(not np.isclose(x, 90.0) for x in original_lattice.get_angles("degree")) + will_run_uff_md = always_run_md or any( + (not np.isclose(original_lattice.vectors[i][j], 0) for i in range(3) for j in range(3) if i != j) + ) + log(f"will_run_uff_md: {will_run_uff_md}", loglevel) + if will_run_uff_md: + if np.linalg.det(original_lattice.vectors) < 0: + raise PackMolError("packmol_around cannot handle lattice where the determinant of the vectors is negative.") + + target_lattice = np.diag(maxcomponents) - # we now know how many molecules to pack around the current molecule + system_for_packing_type = "supercell" # "supercell" or "distorted" + # system_for_packing_type = "distorted" # "supercell" or "distorted" - distorted = original_ucs.copy() - distorted.lattice.vectors = np.diag(maxcomponents) - distorted.set_fractional_coordinates(original_frac_coords) - # remove bonds to be able to do "shake all bonds * *" for the remaining molecules - if will_run_uff_md: - distorted.bonds.clear_bonds() - distorted_with_molecules = [chemsys_to_plams_molecule(distorted)] + tolist(molecules) n_molecules = [1] + details["n_molecules"] + if system_for_packing_type == "supercell": + # Create a supercell that should encompass the target x/y/z lattice + # this is used for the initial packing of molecules to ensure there is no overlap + # with the original atoms + supercell = original_ucs.copy() + trafo = np.linalg.inv(original_ucs.lattice.vectors) @ np.array(target_lattice) + trafo = np.sign(trafo) * np.ceil(np.abs(trafo)) + trafo = np.int_(trafo) + supercell.supercell_trafo(trafo) + supercell.map_atoms(0) + system_for_packing = supercell + else: + # now distort the original system to the target lattice + distorted = original_ucs.copy() + distorted.lattice.vectors = np.diag(maxcomponents) + distorted.set_fractional_coordinates(original_frac_coords) + system_for_packing = distorted + # in general we need higher tolerance here since we may be expanding the original system, # and we do not want the added molecules to enter in artificial "voids" - tolerance = kwargs.get("tolerance", 2.2) + tolerance = kwargs.get("tolerance", 1.5) * 1.3 # should depend on distortion_vol_expansion_factor somehow + log(f"{system_for_packing_type=}", loglevel) + log(f"{n_molecules=}, {box_bounds=}, {tolerance=}", loglevel) my_packed, details = packmol( - molecules=distorted_with_molecules, + molecules=[chemsys_to_plams_molecule(system_for_packing)] + tolist(molecules), n_molecules=n_molecules, fix_first=True, box_bounds=box_bounds, return_details=True, tolerance=tolerance, ) - if will_run_uff_md: - nsteps = 500 - - s = Settings() - s.input.ForceField.Type = "UFF" - s.input.ams.Task = "MolecularDynamics" - s.input.ams.Constraints.AtomList = " ".join(str(x + 1) for x in range(len(current))) - s.input.ams.MolecularDynamics.NSteps = nsteps - s.input.ams.MolecularDynamics.TimeStep = 0.5 - s.input.ams.MolecularDynamics.InitialVelocities.Temperature = 10 - s.input.ams.MolecularDynamics.Shake.All = "bonds * *" - - l = original_lattice.vectors - target_lattice_str = f""" - {l[0][0]} {l[0][1]} {l[0][2]} - {l[1][0]} {l[1][1]} {l[1][2]} - {l[2][0]} {l[2][1]} {l[2][2]} - """ + # remove the original substrate + my_packed = plams_molecule_to_chemsys(my_packed) + my_packed.remove_atoms(range(len(system_for_packing))) + my_packed.map_atoms_continuous() + my_packed.lattice = Lattice() # so that we can add_other without having incompatible lattices - s.input.ams.MolecularDynamics.Deformation.StartStep = 1 - s.input.ams.MolecularDynamics.Deformation.TargetLattice._1 = target_lattice_str + # now create a distorted system + distorted = original_ucs.copy() + distorted.lattice.vectors = np.diag(maxcomponents) + distorted.set_fractional_coordinates(original_frac_coords) - job = AMSJob(settings=s, molecule=my_packed, name="shakemd") - job.run() - my_packed = job.results.get_main_molecule() - delete_job(job) + # distortion_vol_expansion_factor will be used to modify the tolerance when doing the packing + # distortion_vol_expansion_factor = (distorted.lattice.get_volume() / original_volume) ** (1 / 3.0) + # remove bonds to be able to do "shake all bonds * *" for the remaining molecules + if will_run_uff_md: + distorted.bonds.clear_bonds() + distorted.add_other(my_packed) + + if will_run_uff_md: + log("Running UFF MD", loglevel) + distorted = _run_uff_md( + distorted, + nsteps=1500, + vectors=original_ucs.lattice.vectors, + fixed_atoms=list(range(len(original_ucs))), + keepjob=True, + ) - my_packed_ucs = plams_molecule_to_chemsys(my_packed) - my_packed_ucs.remove_atoms(range(len(original_ucs))) - my_packed_ucs.map_atoms_continuous() # so that we can add_other without having incompatible lattices - my_packed_ucs.lattice = Lattice() + distorted.remove_atoms(range(len(original_ucs))) + distorted.map_atoms_continuous() + distorted.lattice = Lattice() # so that we can add_other without having incompatible lattices + # this ensures that the original UCS is exactly preserved (including bonds etc.) out_ucs = original_ucs.copy() - out_ucs.add_other(my_packed_ucs) + out_ucs.add_other(distorted) out_ucs.map_atoms(0) out_mol = chemsys_to_plams_molecule(out_ucs) From 5b22913ac952c58ac536004583b105a27935f104 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Matti=20Hellstr=C3=B6m?= Date: Tue, 3 Dec 2024 16:12:33 +0100 Subject: [PATCH 03/10] add type hints for Molecule.get_mass and get_density SO-- --- mol/molecule.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/mol/molecule.py b/mol/molecule.py index 4f82d1db..3a9ccb2e 100644 --- a/mol/molecule.py +++ b/mol/molecule.py @@ -2000,11 +2000,11 @@ def get_masses(self, unit: Optional[str] = "amu"): unit_conversion_coeff = Units.convert(1.0, "amu", unit) return [at.mass * unit_conversion_coeff for at in self.atoms] - def get_mass(self, unit="amu"): + def get_mass(self, unit="amu") -> float: """Return the mass of the molecule, by default in atomic mass units.""" return sum([at.mass for at in self.atoms]) * Units.convert(1.0, "amu", unit) - def get_density(self): + def get_density(self) -> float: """Return the density in kg/m^3""" vol = self.unit_cell_volume(unit="angstrom") * 1e-30 # in m^3 mass = self.get_mass(unit="kg") From 1f12aa3edadde3ddef56d12cba9d1758507227c7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Matti=20Hellstr=C3=B6m?= Date: Tue, 3 Dec 2024 16:39:58 +0100 Subject: [PATCH 04/10] format examples/PackMol.py with black SO-- --- examples/PackMol.py | 58 ++++++++++++++++++++++----------------------- 1 file changed, 29 insertions(+), 29 deletions(-) diff --git a/examples/PackMol.py b/examples/PackMol.py index 59f8cb6e..10830b39 100644 --- a/examples/PackMol.py +++ b/examples/PackMol.py @@ -12,6 +12,7 @@ # ## Helper functions + def printsummary(mol, details=None): if details: density = details["density"] @@ -30,35 +31,35 @@ def printsummary(mol, details=None): # First, create the gasphase molecule: water = from_smiles("O") -plot_molecule(water); +plot_molecule(water) print("pure liquid from approximate number of atoms and exact density (in g/cm^3), cubic box with auto-determined size") out = packmol(water, n_atoms=194, density=1.0) printsummary(out) out.write("water-1.xyz") -plot_molecule(out); +plot_molecule(out) print("pure liquid from approximate density (in g/cm^3) and an orthorhombic box") out = packmol(water, density=1.0, box_bounds=[0.0, 0.0, 0.0, 8.0, 12.0, 14.0]) printsummary(out) out.write("water-2.xyz") -plot_molecule(out); +plot_molecule(out) print("pure liquid with explicit number of molecules and exact density") out = packmol(water, n_molecules=64, density=1.0) printsummary(out) out.write("water-3.xyz") -plot_molecule(out); +plot_molecule(out) print("pure liquid with explicit number of molecules and box") out = packmol(water, n_molecules=64, box_bounds=[0.0, 0.0, 0.0, 12.0, 13.0, 14.0]) printsummary(out) out.write("water-4.xyz") -plot_molecule(out); +plot_molecule(out) print("water-5.xyz: pure liquid in non-orthorhombic box (requires AMS2025 or later)") @@ -73,14 +74,14 @@ def printsummary(mol, details=None): box.lattice = [[10.0, 2.0, -1.0], [-5.0, 8.0, 0.0], [0.0, -2.0, 11.0]] out = packmol_around(box, molecules=[water], n_molecules=[32]) out.write("water-5.xyz") -plot_molecule(out); +plot_molecule(out) print("Experimental feature (AMS2025): guess density for pure liquid") print("Note: This density is meant to be equilibrated with NPT MD. It can be very inaccurate!") out = packmol(water, n_atoms=100) print(f"Guessed density: {out.get_density():.2f} kg/m^3") -plot_molecule(out); +plot_molecule(out) # ## Water-acetonitrile mixture (fluid with 2 or more components) @@ -119,7 +120,7 @@ def printsummary(mol, details=None): ) printsummary(out, details) out.write("water-acetonitrile-1.xyz") -plot_molecule(out); +plot_molecule(out) # The ``details`` is a dictionary as follows: @@ -138,7 +139,7 @@ def printsummary(mol, details=None): ) printsummary(out, details) out.write("water-acetonitrile-2.xyz") -plot_molecule(out); +plot_molecule(out) print("2-1 water-acetonitrile from explicit number of molecules and density, cubic box with auto-determined size") @@ -150,7 +151,7 @@ def printsummary(mol, details=None): ) printsummary(out, details) out.write("water-acetonitrile-3.xyz") -plot_molecule(out); +plot_molecule(out) print("2-1 water-acetonitrile from explicit number of molecules and box") @@ -161,18 +162,18 @@ def printsummary(mol, details=None): ) printsummary(out) out.write("water-acetonitrile-4.xyz") -plot_molecule(out); +plot_molecule(out) print("Experimental feature (AMS2025): guess density for mixture") print("Note: This density is meant to be equilibrated with NPT MD. It can be very inaccurate!") out = packmol([water, acetonitrile], mole_fractions=[x_water, x_acetonitrile], n_atoms=100) print(f"Guessed density: {out.get_density():.2f} kg/m^3") -plot_molecule(out); +plot_molecule(out) # ## Pack inside sphere -# +# # Set ``sphere=True`` to pack in a sphere (non-periodic) instead of in a periodic box. The sphere will be centered near the origin. print("water in a sphere from exact density and number of molecules") @@ -181,7 +182,7 @@ def printsummary(mol, details=None): print(f"Radius of sphere: {details['radius']:.3f} ang.") print(f"Center of mass xyz (ang): {out.get_center_of_mass()}") out.write("water-sphere.xyz") -plot_molecule(out); +plot_molecule(out) print( @@ -198,13 +199,13 @@ def printsummary(mol, details=None): ) printsummary(out, details) out.write("water-acetonitrile-sphere.xyz") -plot_molecule(out); +plot_molecule(out) # ## Packing ions, total system charge -# +# # The total system charge will be sum of the charges of the constituent molecules. -# +# # In PLAMS, ``molecule.properties.charge`` specifies the charge: ammonium = from_smiles("[NH4+]") # ammonia.properties.charge == +1 @@ -218,7 +219,7 @@ def printsummary(mol, details=None): tot_charge = out.properties.get("charge", 0) print(f"Total charge of packmol-generated system: {tot_charge}") out.write("water-ammonium-chloride.xyz") -plot_molecule(out); +plot_molecule(out) # ## Microsolvation @@ -232,7 +233,7 @@ def printsummary(mol, details=None): out.write("acetonitrile-microsolvated.xyz") figsize = (3, 3) -plot_molecule(out, figsize=figsize); +plot_molecule(out, figsize=figsize) # ## Solid-liquid or solid-gas interfaces @@ -243,21 +244,21 @@ def printsummary(mol, details=None): rotation = "90x,0y,0z" # sideview of slab slab = fromASE(fcc111("Al", size=(4, 6, 3), vacuum=15.0, orthogonal=True, periodic=True)) -plot_molecule(slab, figsize=figsize, rotation=rotation); +plot_molecule(slab, figsize=figsize, rotation=rotation) print("water surrounding an Al slab, from an approximate density") out = packmol_around(slab, water, density=1.0) printsummary(out) out.write("al-water-pure.xyz") -plot_molecule(out, figsize=figsize, rotation=rotation); +plot_molecule(out, figsize=figsize, rotation=rotation) print("2-1 water-acetonitrile mixture surrounding an Al slab, from mole fractions and an approximate density") out = packmol_around(slab, [water, acetonitrile], mole_fractions=[x_water, x_acetonitrile], density=density) printsummary(out) out.write("al-water-acetonitrile.xyz") -plot_molecule(out, figsize=figsize, rotation=rotation); +plot_molecule(out, figsize=figsize, rotation=rotation) from ase.build import surface @@ -273,7 +274,7 @@ def printsummary(mol, details=None): # ## Pack inside voids in crystals -# +# # Use the ``packmol_around`` function. You can decrease ``tolerance`` if you need to pack very tightly. The default value for ``tolerance`` is 2.0. from scm.plams import fromASE @@ -281,7 +282,7 @@ def printsummary(mol, details=None): bulk_Al = fromASE(bulk("Al", cubic=True).repeat((3, 3, 3))) rotation = "-85x,5y,0z" -plot_molecule(bulk_Al, rotation=rotation, radii=0.4); +plot_molecule(bulk_Al, rotation=rotation, radii=0.4) out = packmol_around( @@ -296,9 +297,9 @@ def printsummary(mol, details=None): # ## Bonds, atom properties (force field types, regions, ...) -# +# # The ``packmol()`` function accepts the arguments ``keep_bonds`` and ``keep_atom_properties``. These options will keep the bonds defined for the constitutent molecules, as well as any atomic properties. -# +# # The bonds and atom properties are easiest to see by printing the System block for an AMS job: water = from_smiles("O") @@ -321,7 +322,7 @@ def printsummary(mol, details=None): print(AMSJob(molecule=out).get_input()) -# By default, the ``packmol()`` function assigns regions called ``mol0``, ``mol1``, etc. to the different added molecules. The ``region_names`` option lets you set custom names. +# By default, the ``packmol()`` function assigns regions called ``mol0``, ``mol1``, etc. to the different added molecules. The ``region_names`` option lets you set custom names. out = packmol( [water, n2], @@ -332,7 +333,7 @@ def printsummary(mol, details=None): print(AMSJob(molecule=out).get_input()) -# Below, we also set ``keep_atom_properties=False``, this will remove the previous regions (in this example "oxygen_atom") and mass. +# Below, we also set ``keep_atom_properties=False``, this will remove the previous regions (in this example "oxygen_atom") and mass. out = packmol([water, n2], n_molecules=[2, 1], density=0.5, keep_atom_properties=False) print(AMSJob(molecule=out).get_input()) @@ -349,4 +350,3 @@ def printsummary(mol, details=None): keep_atom_properties=False, ) print(AMSJob(molecule=out).get_input()) - From 471d7ca152b2b6bcd20e20f752594844f499203e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Matti=20Hellstr=C3=B6m?= Date: Tue, 3 Dec 2024 16:57:20 +0100 Subject: [PATCH 05/10] remove unused imports SO-- --- interfaces/molecule/packmol.py | 10 ++-------- 1 file changed, 2 insertions(+), 8 deletions(-) diff --git a/interfaces/molecule/packmol.py b/interfaces/molecule/packmol.py index e47fa098..bb148a91 100644 --- a/interfaces/molecule/packmol.py +++ b/interfaces/molecule/packmol.py @@ -16,12 +16,7 @@ if TYPE_CHECKING: try: - from scm.libbase import ( - UnifiedChemicalSystem as ChemicalSystem, - UnifiedElement as Element, - UnifiedElements as Elements, - UnifiedLattice as Lattice, - ) + from scm.libbase import UnifiedChemicalSystem as ChemicalSystem except ImportError: pass @@ -784,8 +779,7 @@ def _run_uff_md( Raises: PackmolError if something goes worng. """ - from scm.plams.interfaces.adfsuite.quickjobs import _ensure_init - from scm.plams.core.functions import finish, delete_job + from scm.plams.core.functions import delete_job from scm.plams import config thermostatted_region = "PACKMOL_thermostatted" From 82dfb7d47011e8e83db1018942991f63a299ebe8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Matti=20Hellstr=C3=B6m?= Date: Fri, 13 Dec 2024 12:23:10 +0100 Subject: [PATCH 06/10] packmol_around docs, clean up workdir SO103 --- CHANGELOG.md | 1 + doc/source/components/mol_packmol.rst | 3 + .../examples/PackMolExample/PackMol.ipynb | 4 -- .../PackMolExample/PackMol.rst.include | 4 -- doc/source/general.rst | 1 + examples/PackMol.py | 62 +++++++++---------- interfaces/molecule/packmol.py | 47 +++++++------- 7 files changed, 60 insertions(+), 62 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 9347829b..8787200f 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -16,6 +16,7 @@ This changelog is effective from the 2025 releases. * `AMSJob` can accept an AMS `ChemicalSystem` instead of a PLAMS `Molecule` as an input system * Specific `ConfigSettings` and related settings classes with explicitly defined fields * Support for work functions: `AMSResults.get_work_function_results` and `plot_work_function` +* New `packmol_around` function for packing in non-orthorhombic boxes. * Example on `MoleculeFormats` * Script `generate_example.sh` to generate documentation pages from notebook examples * GitHub workflows for CI and publishing to PyPI diff --git a/doc/source/components/mol_packmol.rst b/doc/source/components/mol_packmol.rst index 04681070..452ab123 100644 --- a/doc/source/components/mol_packmol.rst +++ b/doc/source/components/mol_packmol.rst @@ -13,6 +13,7 @@ Packmol (`Packmol website `__) is The following functions eixst: * ``packmol`` (for fluids with 1 or more components) +* ``packmol_around`` (for fluids with 1 or more components, used to pack around an existing system in AMS2025+) * ``packmol_on_slab`` (for solid/liquid or solid/gas interfaces with 1 or more components in the fluid) * ``packmol_in_void`` (for packing molecules inside crystal voids) * ``packmol_microsolvation`` (for microsolvation of a solute with a solvent) @@ -27,6 +28,8 @@ the packmol program included with the Amsterdam Modeling Suite will be used. .. autofunction:: packmol +.. autofunction:: packmol_around + .. autofunction:: packmol_on_slab .. autofunction:: packmol_in_void diff --git a/doc/source/examples/PackMolExample/PackMol.ipynb b/doc/source/examples/PackMolExample/PackMol.ipynb index 97634737..dbfa4ab7 100644 --- a/doc/source/examples/PackMolExample/PackMol.ipynb +++ b/doc/source/examples/PackMolExample/PackMol.ipynb @@ -255,11 +255,7 @@ "print(\"water-5.xyz: pure liquid in non-orthorhombic box (requires AMS2025 or later)\")\n", "# Non-orthorhombic boxes use UFF MD simulations behind the scenes\n", "# You can pack inside any lattice using the packmol_around function\n", - "from scm.plams import init, Settings\n", "\n", - "s = Settings()\n", - "s.log.stdout = 0\n", - "init(config_settings=s)\n", "box = Molecule()\n", "box.lattice = [[10.0, 2.0, -1.0], [-5.0, 8.0, 0.0], [0.0, -2.0, 11.0]]\n", "out = packmol_around(box, molecules=[water], n_molecules=[32])\n", diff --git a/doc/source/examples/PackMolExample/PackMol.rst.include b/doc/source/examples/PackMolExample/PackMol.rst.include index a38d51bb..c36da26d 100644 --- a/doc/source/examples/PackMolExample/PackMol.rst.include +++ b/doc/source/examples/PackMolExample/PackMol.rst.include @@ -123,11 +123,7 @@ First, create the gasphase molecule: print("water-5.xyz: pure liquid in non-orthorhombic box (requires AMS2025 or later)") # Non-orthorhombic boxes use UFF MD simulations behind the scenes # You can pack inside any lattice using the packmol_around function - from scm.plams import init, Settings - s = Settings() - s.log.stdout = 0 - init(config_settings=s) box = Molecule() box.lattice = [[10.0, 2.0, -1.0], [-5.0, 8.0, 0.0], [0.0, -2.0, 11.0]] out = packmol_around(box, molecules=[water], n_molecules=[32]) diff --git a/doc/source/general.rst b/doc/source/general.rst index b5d49ae1..ad525721 100644 --- a/doc/source/general.rst +++ b/doc/source/general.rst @@ -112,6 +112,7 @@ Added * Support for AMS ``ChemicalSystem`` within |AMSJob| and |AMSResults|. |AMSJob| can accept a ``ChemicalSystem`` as an input system, and the methods :meth:`~scm.plams.interfaces.adfsuite.ams.AMSResults.get_system`, :meth:`~scm.plams.interfaces.adfsuite.ams.AMSResults.get_input_system` and :meth:`~scm.plams.interfaces.adfsuite.ams.AMSResults.get_main_system` on |AMSResults| return a ``ChemicalSystem``. These provide the option to use a ``ChemicalSystem`` in place of a PLAMS ``Molecule``. * Support for work functions through :meth:`~scm.plams.interfaces.adfsuite.ams.AMSResults.get_work_function_results` and :func:`~scm.plams.tools.plot.plot_work_function` +* Added :meth:`~scm.plams.interfaces.molecule.packmol.packmol_around` method to pack around an existing molecule or pack molecules into a non-orthorhombic box. Changed ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/examples/PackMol.py b/examples/PackMol.py index 10830b39..d20a7143 100644 --- a/examples/PackMol.py +++ b/examples/PackMol.py @@ -12,7 +12,6 @@ # ## Helper functions - def printsummary(mol, details=None): if details: density = details["density"] @@ -31,57 +30,53 @@ def printsummary(mol, details=None): # First, create the gasphase molecule: water = from_smiles("O") -plot_molecule(water) +plot_molecule(water); print("pure liquid from approximate number of atoms and exact density (in g/cm^3), cubic box with auto-determined size") out = packmol(water, n_atoms=194, density=1.0) printsummary(out) out.write("water-1.xyz") -plot_molecule(out) +plot_molecule(out); print("pure liquid from approximate density (in g/cm^3) and an orthorhombic box") out = packmol(water, density=1.0, box_bounds=[0.0, 0.0, 0.0, 8.0, 12.0, 14.0]) printsummary(out) out.write("water-2.xyz") -plot_molecule(out) +plot_molecule(out); print("pure liquid with explicit number of molecules and exact density") out = packmol(water, n_molecules=64, density=1.0) printsummary(out) out.write("water-3.xyz") -plot_molecule(out) +plot_molecule(out); print("pure liquid with explicit number of molecules and box") out = packmol(water, n_molecules=64, box_bounds=[0.0, 0.0, 0.0, 12.0, 13.0, 14.0]) printsummary(out) out.write("water-4.xyz") -plot_molecule(out) +plot_molecule(out); print("water-5.xyz: pure liquid in non-orthorhombic box (requires AMS2025 or later)") # Non-orthorhombic boxes use UFF MD simulations behind the scenes # You can pack inside any lattice using the packmol_around function -from scm.plams import init, Settings -s = Settings() -s.log.stdout = 0 -init(config_settings=s) box = Molecule() box.lattice = [[10.0, 2.0, -1.0], [-5.0, 8.0, 0.0], [0.0, -2.0, 11.0]] out = packmol_around(box, molecules=[water], n_molecules=[32]) out.write("water-5.xyz") -plot_molecule(out) +plot_molecule(out); print("Experimental feature (AMS2025): guess density for pure liquid") print("Note: This density is meant to be equilibrated with NPT MD. It can be very inaccurate!") out = packmol(water, n_atoms=100) print(f"Guessed density: {out.get_density():.2f} kg/m^3") -plot_molecule(out) +plot_molecule(out); # ## Water-acetonitrile mixture (fluid with 2 or more components) @@ -120,7 +115,7 @@ def printsummary(mol, details=None): ) printsummary(out, details) out.write("water-acetonitrile-1.xyz") -plot_molecule(out) +plot_molecule(out); # The ``details`` is a dictionary as follows: @@ -139,7 +134,7 @@ def printsummary(mol, details=None): ) printsummary(out, details) out.write("water-acetonitrile-2.xyz") -plot_molecule(out) +plot_molecule(out); print("2-1 water-acetonitrile from explicit number of molecules and density, cubic box with auto-determined size") @@ -151,7 +146,7 @@ def printsummary(mol, details=None): ) printsummary(out, details) out.write("water-acetonitrile-3.xyz") -plot_molecule(out) +plot_molecule(out); print("2-1 water-acetonitrile from explicit number of molecules and box") @@ -162,18 +157,18 @@ def printsummary(mol, details=None): ) printsummary(out) out.write("water-acetonitrile-4.xyz") -plot_molecule(out) +plot_molecule(out); print("Experimental feature (AMS2025): guess density for mixture") print("Note: This density is meant to be equilibrated with NPT MD. It can be very inaccurate!") out = packmol([water, acetonitrile], mole_fractions=[x_water, x_acetonitrile], n_atoms=100) print(f"Guessed density: {out.get_density():.2f} kg/m^3") -plot_molecule(out) +plot_molecule(out); # ## Pack inside sphere -# +# # Set ``sphere=True`` to pack in a sphere (non-periodic) instead of in a periodic box. The sphere will be centered near the origin. print("water in a sphere from exact density and number of molecules") @@ -182,7 +177,7 @@ def printsummary(mol, details=None): print(f"Radius of sphere: {details['radius']:.3f} ang.") print(f"Center of mass xyz (ang): {out.get_center_of_mass()}") out.write("water-sphere.xyz") -plot_molecule(out) +plot_molecule(out); print( @@ -199,13 +194,13 @@ def printsummary(mol, details=None): ) printsummary(out, details) out.write("water-acetonitrile-sphere.xyz") -plot_molecule(out) +plot_molecule(out); # ## Packing ions, total system charge -# +# # The total system charge will be sum of the charges of the constituent molecules. -# +# # In PLAMS, ``molecule.properties.charge`` specifies the charge: ammonium = from_smiles("[NH4+]") # ammonia.properties.charge == +1 @@ -219,7 +214,7 @@ def printsummary(mol, details=None): tot_charge = out.properties.get("charge", 0) print(f"Total charge of packmol-generated system: {tot_charge}") out.write("water-ammonium-chloride.xyz") -plot_molecule(out) +plot_molecule(out); # ## Microsolvation @@ -233,7 +228,7 @@ def printsummary(mol, details=None): out.write("acetonitrile-microsolvated.xyz") figsize = (3, 3) -plot_molecule(out, figsize=figsize) +plot_molecule(out, figsize=figsize); # ## Solid-liquid or solid-gas interfaces @@ -244,21 +239,21 @@ def printsummary(mol, details=None): rotation = "90x,0y,0z" # sideview of slab slab = fromASE(fcc111("Al", size=(4, 6, 3), vacuum=15.0, orthogonal=True, periodic=True)) -plot_molecule(slab, figsize=figsize, rotation=rotation) +plot_molecule(slab, figsize=figsize, rotation=rotation); print("water surrounding an Al slab, from an approximate density") out = packmol_around(slab, water, density=1.0) printsummary(out) out.write("al-water-pure.xyz") -plot_molecule(out, figsize=figsize, rotation=rotation) +plot_molecule(out, figsize=figsize, rotation=rotation); print("2-1 water-acetonitrile mixture surrounding an Al slab, from mole fractions and an approximate density") out = packmol_around(slab, [water, acetonitrile], mole_fractions=[x_water, x_acetonitrile], density=density) printsummary(out) out.write("al-water-acetonitrile.xyz") -plot_molecule(out, figsize=figsize, rotation=rotation) +plot_molecule(out, figsize=figsize, rotation=rotation); from ase.build import surface @@ -274,7 +269,7 @@ def printsummary(mol, details=None): # ## Pack inside voids in crystals -# +# # Use the ``packmol_around`` function. You can decrease ``tolerance`` if you need to pack very tightly. The default value for ``tolerance`` is 2.0. from scm.plams import fromASE @@ -282,7 +277,7 @@ def printsummary(mol, details=None): bulk_Al = fromASE(bulk("Al", cubic=True).repeat((3, 3, 3))) rotation = "-85x,5y,0z" -plot_molecule(bulk_Al, rotation=rotation, radii=0.4) +plot_molecule(bulk_Al, rotation=rotation, radii=0.4); out = packmol_around( @@ -297,9 +292,9 @@ def printsummary(mol, details=None): # ## Bonds, atom properties (force field types, regions, ...) -# +# # The ``packmol()`` function accepts the arguments ``keep_bonds`` and ``keep_atom_properties``. These options will keep the bonds defined for the constitutent molecules, as well as any atomic properties. -# +# # The bonds and atom properties are easiest to see by printing the System block for an AMS job: water = from_smiles("O") @@ -322,7 +317,7 @@ def printsummary(mol, details=None): print(AMSJob(molecule=out).get_input()) -# By default, the ``packmol()`` function assigns regions called ``mol0``, ``mol1``, etc. to the different added molecules. The ``region_names`` option lets you set custom names. +# By default, the ``packmol()`` function assigns regions called ``mol0``, ``mol1``, etc. to the different added molecules. The ``region_names`` option lets you set custom names. out = packmol( [water, n2], @@ -333,7 +328,7 @@ def printsummary(mol, details=None): print(AMSJob(molecule=out).get_input()) -# Below, we also set ``keep_atom_properties=False``, this will remove the previous regions (in this example "oxygen_atom") and mass. +# Below, we also set ``keep_atom_properties=False``, this will remove the previous regions (in this example "oxygen_atom") and mass. out = packmol([water, n2], n_molecules=[2, 1], density=0.5, keep_atom_properties=False) print(AMSJob(molecule=out).get_input()) @@ -350,3 +345,4 @@ def printsummary(mol, details=None): keep_atom_properties=False, ) print(AMSJob(molecule=out).get_input()) + diff --git a/interfaces/molecule/packmol.py b/interfaces/molecule/packmol.py index bb148a91..48671118 100644 --- a/interfaces/molecule/packmol.py +++ b/interfaces/molecule/packmol.py @@ -13,6 +13,7 @@ from scm.plams.interfaces.molecule.rdkit import readpdb, writepdb from scm.plams.core.functions import requires_optional_package, log from scm.plams.core.settings import Settings +from scm.plams.core.jobmanager import JobManager if TYPE_CHECKING: try: @@ -816,24 +817,27 @@ def _run_uff_md( s.input.ams.MolecularDynamics.Deformation.TargetLattice._1 = target_lattice_str previous_config = config.copy() - config.job.pickle = False - config.log.stdout = 0 - # TODO: fix this so that it doesn't leave plams_workdir on disk if it is created - job = AMSJob(settings=s, molecule=md_ucs, name="shakemd") - job.run() - job.results.wait() - config.job.pickle = previous_config.job.pickle - config.log.stdout = previous_config.log.stdout - if not job.ok(): - raise PackMolError( - f"Try a lower density or a less skewed cell! Original file in {job.path} . " - + str(job.results.get_errormsg()) - ) - my_packed = job.results.get_main_system() - if not keepjob: - delete_job(job) + try: + config.job.pickle = False + config.log.stdout = 0 + + with tempfile.TemporaryDirectory() as tmp_dir: + job_manager = JobManager(config.jobmanager, path=tmp_dir) + job = AMSJob(settings=s, molecule=md_ucs, name="shakemd") + job.run(jobmanager=job_manager) + + if not job.ok(): + raise PackMolError( + f"Try a lower density or a less skewed cell! Original file in {job.path} . " + + str(job.results.get_errormsg()) + ) + my_packed = job.results.get_main_system() + my_packed.remove_region("PACKMOL_thermostatted") + + finally: + config.job.pickle = previous_config.job.pickle + config.log.stdout = previous_config.log.stdout - my_packed.remove_region("PACKMOL_thermostatted") return my_packed @@ -855,7 +859,7 @@ def packmol_around( For all other arguments, see the ``packmol`` function. - In the returned ``Molecule`, the system will be mapped to [0..1]. It has the same lattice has ``current``. + In the returned ``Molecule``, the system will be mapped to ``[0..1]``. It has the same lattice has ``current``. """ from scm.libbase import ( UnifiedChemicalSystem as ChemicalSystem, @@ -924,16 +928,17 @@ def packmol_around( supercell.supercell_trafo(trafo) supercell.map_atoms(0) system_for_packing = supercell + tolerance = kwargs.get("tolerance", 1.5) else: # now distort the original system to the target lattice distorted = original_ucs.copy() distorted.lattice.vectors = np.diag(maxcomponents) distorted.set_fractional_coordinates(original_frac_coords) system_for_packing = distorted + # in general we need higher tolerance here since we may be expanding the original system, + # and we do not want the added molecules to enter in artificial "voids" + tolerance = kwargs.get("tolerance", 1.5) * 1.3 # should depend on distortion_vol_expansion_factor somehow - # in general we need higher tolerance here since we may be expanding the original system, - # and we do not want the added molecules to enter in artificial "voids" - tolerance = kwargs.get("tolerance", 1.5) * 1.3 # should depend on distortion_vol_expansion_factor somehow log(f"{system_for_packing_type=}", loglevel) log(f"{n_molecules=}, {box_bounds=}, {tolerance=}", loglevel) my_packed, details = packmol( From 0adf2ec624fef70ba41cf6b6840cfa5e5ebf619d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Matti=20Hellstr=C3=B6m?= Date: Fri, 13 Dec 2024 12:27:08 +0100 Subject: [PATCH 07/10] fix black formatting PackMol.py SLO-- --- examples/PackMol.py | 58 ++++++++++++++++++++++----------------------- 1 file changed, 29 insertions(+), 29 deletions(-) diff --git a/examples/PackMol.py b/examples/PackMol.py index d20a7143..0e366ad3 100644 --- a/examples/PackMol.py +++ b/examples/PackMol.py @@ -12,6 +12,7 @@ # ## Helper functions + def printsummary(mol, details=None): if details: density = details["density"] @@ -30,35 +31,35 @@ def printsummary(mol, details=None): # First, create the gasphase molecule: water = from_smiles("O") -plot_molecule(water); +plot_molecule(water) print("pure liquid from approximate number of atoms and exact density (in g/cm^3), cubic box with auto-determined size") out = packmol(water, n_atoms=194, density=1.0) printsummary(out) out.write("water-1.xyz") -plot_molecule(out); +plot_molecule(out) print("pure liquid from approximate density (in g/cm^3) and an orthorhombic box") out = packmol(water, density=1.0, box_bounds=[0.0, 0.0, 0.0, 8.0, 12.0, 14.0]) printsummary(out) out.write("water-2.xyz") -plot_molecule(out); +plot_molecule(out) print("pure liquid with explicit number of molecules and exact density") out = packmol(water, n_molecules=64, density=1.0) printsummary(out) out.write("water-3.xyz") -plot_molecule(out); +plot_molecule(out) print("pure liquid with explicit number of molecules and box") out = packmol(water, n_molecules=64, box_bounds=[0.0, 0.0, 0.0, 12.0, 13.0, 14.0]) printsummary(out) out.write("water-4.xyz") -plot_molecule(out); +plot_molecule(out) print("water-5.xyz: pure liquid in non-orthorhombic box (requires AMS2025 or later)") @@ -69,14 +70,14 @@ def printsummary(mol, details=None): box.lattice = [[10.0, 2.0, -1.0], [-5.0, 8.0, 0.0], [0.0, -2.0, 11.0]] out = packmol_around(box, molecules=[water], n_molecules=[32]) out.write("water-5.xyz") -plot_molecule(out); +plot_molecule(out) print("Experimental feature (AMS2025): guess density for pure liquid") print("Note: This density is meant to be equilibrated with NPT MD. It can be very inaccurate!") out = packmol(water, n_atoms=100) print(f"Guessed density: {out.get_density():.2f} kg/m^3") -plot_molecule(out); +plot_molecule(out) # ## Water-acetonitrile mixture (fluid with 2 or more components) @@ -115,7 +116,7 @@ def printsummary(mol, details=None): ) printsummary(out, details) out.write("water-acetonitrile-1.xyz") -plot_molecule(out); +plot_molecule(out) # The ``details`` is a dictionary as follows: @@ -134,7 +135,7 @@ def printsummary(mol, details=None): ) printsummary(out, details) out.write("water-acetonitrile-2.xyz") -plot_molecule(out); +plot_molecule(out) print("2-1 water-acetonitrile from explicit number of molecules and density, cubic box with auto-determined size") @@ -146,7 +147,7 @@ def printsummary(mol, details=None): ) printsummary(out, details) out.write("water-acetonitrile-3.xyz") -plot_molecule(out); +plot_molecule(out) print("2-1 water-acetonitrile from explicit number of molecules and box") @@ -157,18 +158,18 @@ def printsummary(mol, details=None): ) printsummary(out) out.write("water-acetonitrile-4.xyz") -plot_molecule(out); +plot_molecule(out) print("Experimental feature (AMS2025): guess density for mixture") print("Note: This density is meant to be equilibrated with NPT MD. It can be very inaccurate!") out = packmol([water, acetonitrile], mole_fractions=[x_water, x_acetonitrile], n_atoms=100) print(f"Guessed density: {out.get_density():.2f} kg/m^3") -plot_molecule(out); +plot_molecule(out) # ## Pack inside sphere -# +# # Set ``sphere=True`` to pack in a sphere (non-periodic) instead of in a periodic box. The sphere will be centered near the origin. print("water in a sphere from exact density and number of molecules") @@ -177,7 +178,7 @@ def printsummary(mol, details=None): print(f"Radius of sphere: {details['radius']:.3f} ang.") print(f"Center of mass xyz (ang): {out.get_center_of_mass()}") out.write("water-sphere.xyz") -plot_molecule(out); +plot_molecule(out) print( @@ -194,13 +195,13 @@ def printsummary(mol, details=None): ) printsummary(out, details) out.write("water-acetonitrile-sphere.xyz") -plot_molecule(out); +plot_molecule(out) # ## Packing ions, total system charge -# +# # The total system charge will be sum of the charges of the constituent molecules. -# +# # In PLAMS, ``molecule.properties.charge`` specifies the charge: ammonium = from_smiles("[NH4+]") # ammonia.properties.charge == +1 @@ -214,7 +215,7 @@ def printsummary(mol, details=None): tot_charge = out.properties.get("charge", 0) print(f"Total charge of packmol-generated system: {tot_charge}") out.write("water-ammonium-chloride.xyz") -plot_molecule(out); +plot_molecule(out) # ## Microsolvation @@ -228,7 +229,7 @@ def printsummary(mol, details=None): out.write("acetonitrile-microsolvated.xyz") figsize = (3, 3) -plot_molecule(out, figsize=figsize); +plot_molecule(out, figsize=figsize) # ## Solid-liquid or solid-gas interfaces @@ -239,21 +240,21 @@ def printsummary(mol, details=None): rotation = "90x,0y,0z" # sideview of slab slab = fromASE(fcc111("Al", size=(4, 6, 3), vacuum=15.0, orthogonal=True, periodic=True)) -plot_molecule(slab, figsize=figsize, rotation=rotation); +plot_molecule(slab, figsize=figsize, rotation=rotation) print("water surrounding an Al slab, from an approximate density") out = packmol_around(slab, water, density=1.0) printsummary(out) out.write("al-water-pure.xyz") -plot_molecule(out, figsize=figsize, rotation=rotation); +plot_molecule(out, figsize=figsize, rotation=rotation) print("2-1 water-acetonitrile mixture surrounding an Al slab, from mole fractions and an approximate density") out = packmol_around(slab, [water, acetonitrile], mole_fractions=[x_water, x_acetonitrile], density=density) printsummary(out) out.write("al-water-acetonitrile.xyz") -plot_molecule(out, figsize=figsize, rotation=rotation); +plot_molecule(out, figsize=figsize, rotation=rotation) from ase.build import surface @@ -269,7 +270,7 @@ def printsummary(mol, details=None): # ## Pack inside voids in crystals -# +# # Use the ``packmol_around`` function. You can decrease ``tolerance`` if you need to pack very tightly. The default value for ``tolerance`` is 2.0. from scm.plams import fromASE @@ -277,7 +278,7 @@ def printsummary(mol, details=None): bulk_Al = fromASE(bulk("Al", cubic=True).repeat((3, 3, 3))) rotation = "-85x,5y,0z" -plot_molecule(bulk_Al, rotation=rotation, radii=0.4); +plot_molecule(bulk_Al, rotation=rotation, radii=0.4) out = packmol_around( @@ -292,9 +293,9 @@ def printsummary(mol, details=None): # ## Bonds, atom properties (force field types, regions, ...) -# +# # The ``packmol()`` function accepts the arguments ``keep_bonds`` and ``keep_atom_properties``. These options will keep the bonds defined for the constitutent molecules, as well as any atomic properties. -# +# # The bonds and atom properties are easiest to see by printing the System block for an AMS job: water = from_smiles("O") @@ -317,7 +318,7 @@ def printsummary(mol, details=None): print(AMSJob(molecule=out).get_input()) -# By default, the ``packmol()`` function assigns regions called ``mol0``, ``mol1``, etc. to the different added molecules. The ``region_names`` option lets you set custom names. +# By default, the ``packmol()`` function assigns regions called ``mol0``, ``mol1``, etc. to the different added molecules. The ``region_names`` option lets you set custom names. out = packmol( [water, n2], @@ -328,7 +329,7 @@ def printsummary(mol, details=None): print(AMSJob(molecule=out).get_input()) -# Below, we also set ``keep_atom_properties=False``, this will remove the previous regions (in this example "oxygen_atom") and mass. +# Below, we also set ``keep_atom_properties=False``, this will remove the previous regions (in this example "oxygen_atom") and mass. out = packmol([water, n2], n_molecules=[2, 1], density=0.5, keep_atom_properties=False) print(AMSJob(molecule=out).get_input()) @@ -345,4 +346,3 @@ def printsummary(mol, details=None): keep_atom_properties=False, ) print(AMSJob(molecule=out).get_input()) - From dbb0c144d8d694ed7461ada2725864141ecb05a4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Matti=20Hellstr=C3=B6m?= Date: Fri, 13 Dec 2024 12:29:17 +0100 Subject: [PATCH 08/10] remove unused import SO-- --- interfaces/molecule/packmol.py | 1 - 1 file changed, 1 deletion(-) diff --git a/interfaces/molecule/packmol.py b/interfaces/molecule/packmol.py index 48671118..72d5d33c 100644 --- a/interfaces/molecule/packmol.py +++ b/interfaces/molecule/packmol.py @@ -780,7 +780,6 @@ def _run_uff_md( Raises: PackmolError if something goes worng. """ - from scm.plams.core.functions import delete_job from scm.plams import config thermostatted_region = "PACKMOL_thermostatted" From 9dde844b89205935a60ee2f055ad088c77acf7b3 Mon Sep 17 00:00:00 2001 From: David Ormrod Morley Date: Fri, 13 Dec 2024 15:00:55 +0100 Subject: [PATCH 09/10] Swap to using some numpy features in packmol for clarity SO-- --- interfaces/molecule/packmol.py | 18 ++++++++---------- 1 file changed, 8 insertions(+), 10 deletions(-) diff --git a/interfaces/molecule/packmol.py b/interfaces/molecule/packmol.py index 72d5d33c..49380ca2 100644 --- a/interfaces/molecule/packmol.py +++ b/interfaces/molecule/packmol.py @@ -149,9 +149,7 @@ def get_input_block(self, fname, tolerance): """ elif self.sphere: vol = self.get_volume() - # vol = 4*pi*r^3 /3 - # radius = (3*vol/(4*pi))**0.33333 - radius = (3 * vol / (4 * 3.14159)) ** 0.3333 + radius = np.cbrt(3 * vol / (4 * np.pi)) ret = f""" structure {fname} number {self.n_molecules} @@ -248,7 +246,7 @@ def _get_complete_radius(self) -> float: :rtype: float """ volume = self._get_complete_volume() - radius = (3 * volume / (4 * 3.14159)) ** 0.3333 + radius = np.cbrt(3 * volume / (4 * np.pi)) return radius @@ -313,7 +311,7 @@ def run(self): def sum_of_atomic_volumes(molecule: Molecule) -> float: """Returns the sum of atomic volumes (calculated using vdW radii) in angstrom^3.""" - return (4 / 3) * 3.14159 * sum(at.radius**3 for at in molecule) + return (4 / 3) * np.pi * sum(at.radius**3 for at in molecule) def guess_density(molecules: Sequence[Molecule], coeffs: Sequence[Union[int, float]]) -> float: @@ -340,9 +338,9 @@ def guess_density(molecules: Sequence[Molecule], coeffs: Sequence[Union[int, flo if PeriodicTable.get_metallic(at.symbol): radius *= 0.5 bond_volume_decrease += ( - coeff * bond_volume_decrease_factor * min(len(at.bonds), 0.3) ** 0.3 * (4 / 3) * 3.14159 * radius**3 + coeff * bond_volume_decrease_factor * min(len(at.bonds), 0.3) ** 0.3 * (4 / 3) * np.pi * radius**3 ) - sum_atomic_volumes += coeff * (4 / 3) * 3.14159 * radius**3 # ang^3 + sum_atomic_volumes += coeff * (4 / 3) * np.pi * radius**3 # ang^3 sum_atomic_masses += coeff * PeriodicTable.get_mass(at.symbol) estimated_volume += sum_atomic_volumes - bond_volume_decrease @@ -655,7 +653,7 @@ def packmol( } if sphere: - details["radius"] = (volume * 3 / (4 * 3.1415926535)) ** (1 / 3.0) + details["radius"] = np.cbrt(volume * 3 / (4 * np.pi)) if keep_atom_properties: for at, molecule_type_index, atom_index_in_molecule in zip( @@ -885,12 +883,12 @@ def packmol_around( # step 2, get remaining volume current_atomic_volume = ( - (4 / 3) * 3.14159 * np.sum(np.fromiter((at.element.radius for at in original_ucs), dtype=np.float32)) + (4 / 3) * np.pi * np.sum(np.fromiter((at.element.radius for at in original_ucs), dtype=np.float32)) ) current_atomic_volume /= 0.74 # use packing efficiency in ccp as example to take up more volume remaining_volume = original_volume - current_atomic_volume # temporary value to call the original packmol with - temp_L = remaining_volume ** (1 / 3.0) + temp_L = np.cbrt(remaining_volume) box_bounds_for_remaining_volume = [0.0, 0.0, 0.0, temp_L, temp_L, temp_L] # it is unnecessary to actually pack the molecules, this is just used to get the "details" # details will contain the correct number of molecules to pack in the combined system From 394d665bb95106075aef359aa2fd73dcedbc82e4 Mon Sep 17 00:00:00 2001 From: David Ormrod Morley Date: Fri, 13 Dec 2024 15:04:48 +0100 Subject: [PATCH 10/10] Remove unused arg in _run_uff_md SO-- --- interfaces/molecule/packmol.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/interfaces/molecule/packmol.py b/interfaces/molecule/packmol.py index 49380ca2..d2f1c955 100644 --- a/interfaces/molecule/packmol.py +++ b/interfaces/molecule/packmol.py @@ -768,7 +768,6 @@ def _run_uff_md( nsteps: int = 1000, vectors=None, fixed_atoms: Optional[Sequence[int]] = None, - keepjob: bool = False, ) -> "ChemicalSystem": """ Runs UFF MD with SHAKE all bonds, keeps ``fixed_atoms`` (0-based atom indices) fixed, @@ -971,7 +970,6 @@ def packmol_around( nsteps=1500, vectors=original_ucs.lattice.vectors, fixed_atoms=list(range(len(original_ucs))), - keepjob=True, ) distorted.remove_atoms(range(len(original_ucs))) @@ -1044,7 +1042,7 @@ def packmol_on_slab( # The lattice of the liquid is different ... liquid.lattice = slab.lattice # If the slab has cell-shifts for the bonds, the liquid also needs to have - # them. If if would not have cell-shifts, they would not be updated in the + # them. If would not have cell-shifts, they would not be updated in the # map_to_central_cell call, even though they would become significant when # combining with the slab that has them: minimum image convention is only # assumed if no bond has cell-shifts.