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molmod · Jul 28, 2024 · 2e55f67 · 2e55f67
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diff --git a/README.md b/README.md
@@ -16,7 +16,8 @@ Psiflow is a scalable molecular simulation engine for chemistry and materials sc
 It supports:
 - **quantum mechanical calculations** at various levels of theory (GGA and hybrid DFT, post-HF methods such as MP2 or RPA, and even coupled cluster; using CP2K|GPAW|ORCA)
 - **trainable interaction potentials** as well as easy-to-use universal potentials, e.g. [MACE-MP0](https://arxiv.org/abs/2401.00096)
-- a wide range of **sampling algorithms**: NVE|NVT|NPT, path-integral molecular dynamics, alchemical replica exchange, metadynamics, phonon-based sampling, ...  (thanks to [i-PI](https://ipi-code.org/))
+- a wide range of **sampling algorithms**: NVE | NVT | NPT, path-integral molecular dynamics, alchemical replica exchange, metadynamics, phonon-based sampling, thermodynamic integration; using [i-PI](https://ipi-code.org/),
+[PLUMED](https://www.plumed.org/), ... 
 
 Users may define arbitrarily complex workflows and execute them **automatically** on local, HPC, and/or cloud infrastructure.
 To achieve this, psiflow is built using [Parsl](https://parsl-project.org/): a parallel execution library which manages job submission and workload distribution.

diff --git a/configs/hortense.yaml b/configs/hortense.yaml
@@ -34,7 +34,6 @@ ModelTraining:
 CP2K:
   cores_per_worker: 64
   max_evaluation_time: 30
-  memory_limit: 2GB
   launch_command: 'apptainer exec -e --no-init oras://ghcr.io/molmod/cp2k:2024.1 /opt/entry.sh mpirun -np 32 -bind-to core cp2k.psmp'
   slurm:
     partition: "cpu_rome"

diff --git a/configs/lumi.yaml b/configs/lumi.yaml
@@ -6,7 +6,6 @@ default_threads: 8
 CP2K:
   cores_per_worker: 32
   max_evaluation_time: 20
-  memory_limit: 2GB
   launch_command: 'singularity exec -e --no-init oras://ghcr.io/molmod/cp2k:2024.1 /opt/entry.sh mpirun -np 32 cp2k.psmp'
   slurm:
     partition: "standard"
@@ -30,6 +29,8 @@ ModelEvaluation:
 ModelTraining:
   cores_per_worker: 7
   gpu: true
+  env_vars:
+    OMP_PROC_BIND: "spread"
   slurm:
     partition: "standard-g"
     account: "project_465001125"

diff --git a/configs/threadpool.yaml b/configs/threadpool.yaml
@@ -13,7 +13,6 @@ ModelTraining:
 CP2K:
   cores_per_worker: 2
   max_evaluation_time: 0.3
-  memory_limit: 2GB
   launch_command: 'apptainer exec -e --no-init oras://ghcr.io/molmod/cp2k:2024.1 /opt/entry.sh mpirun -bind-to core -np 2 -env OMP_NUM_THREADS 1 cp2k.psmp'
 CP2K_container:
   cores_per_worker: 2

diff --git a/docs/configuration.md b/docs/configuration.md
@@ -167,8 +167,6 @@ SLURM jobscripts, send them to the scheduler, and once the resources are allocat
 start the calculation. For example, assume that the GPU partition on this cluster is
 named `infinite_a100`, and it has 12 cores per GPU. Consider the following config
 ```yaml
-container_runtime: apptainer   # or singularity; check HPC docs to see which one is available
-container_uri: oras://ghcr.io/molmod/psiflow:main_cu118  # built from github main branch
 ModelTraining:
   cores_per_worker: 12
   gpu: true
@@ -215,7 +213,7 @@ There exist a few additional keywords for `ModelTraining` which might be useful:
     necessary to tune the process/thread affinity a little bit. For example:
     ```yaml
     env_vars:
-      OMP_PROC_BIND: spread
+      OMP_PROC_BIND: "spread"
     ```
 
 ### 2. molecular dynamics

diff --git a/docs/index.md b/docs/index.md
@@ -19,8 +19,14 @@ Users may define arbitrarily complex workflows and execute them **automatically*
 To achieve this, psiflow is built using [Parsl](https://parsl-project.org/): a parallel execution library which manages job submission and workload distribution.
 As such, psiflow can orchestrate large molecular simulation pipelines on hundreds or even thousands of nodes.
 
+<figure markdown="span">
+  ![Image title](overview.png){ width="500" }
+</figure>
+
 ---
 
+# Setup
+
 Use the following one-liner to create a lightweight [micromamba](https://mamba.readthedocs.io/en/latest/user_guide/micromamba.html) Python environment with all dependencies readily available:
 ```sh
 curl -L molmod.github.io/psiflow/install.sh | bash
@@ -48,6 +54,12 @@ For a complete overview of all execution options, see the [configuration](config
 
 - [Solid-state phase stabilities](https://github.com/molmod/psiflow/tree/main/examples/iron_harmonic_fcc_bcc.py) | **iron**: estimating the relative stability of fcc and bcc iron with anharmonic corrections using thermodynamic integration (see e.g. [Phys Rev B., 2018](https://journals.aps.org/prb/abstract/10.1103/PhysRevB.97.054102))
 
+- [ML potentials from scratch](https://github.com/molmod/psiflow/tree/main/examples/online_learning_pimd.py) | **water**: develop an ML potential for water based on a single geometry as input, using a combination of passive and active learning.
+
+- [Replica exchange umbrella sampling](https://github.com/molmod/psiflow/tree/main/examples/proton_jump_plumed.py) |
+  **vinyl alcohol**: explore a reactive transition path with metadynamics, and use the
+  resulting data to perform umbrella sampling with replica exchange between umbrellas.
+
 - [DFT singlepoints](https://github.com/molmod/psiflow/tree/main/examples/water_cp2k_noise.py) | **water**: analysis of the numerical noise DFT energy and force evaluations using CP2K and the RPBE(D3) functional, for a collection of water molecules.
 
 - [Path-integral molecular dynamics](https://github.com/molmod/psiflow/examples/water_path_integral_md.py) | **water**: demonstration of the impact of nuclear quantum effects on the variance in O-H distance in liquid water. Path-integral molecular dynamics simulations with increasing number of beads (1, 2, 4, 8, 16) approximate the proton delocalization, and lead to systematically larger variance in O-H distance.

diff --git a/docs/models.md b/docs/models.md
@@ -47,6 +47,9 @@ rmse = compute_rmse(forces_pred, forces_target)  # this is a Future!
 print('forces RMSE: {} eV/A'.format(rmse.result()))
 
 ```
+Note that `model.save()` will save both a `.yaml` file with all hyperparameters as well as the actual `.pth` model which is needed to reconstruct the corresponding PyTorch module (possibly outside of psiflow if needed).
+As such, it expects a directory as argument (which may either already exist or will be
+created).
 
 In many cases, it is generally recommended to provide these models with some estimate of the absolute energy of an isolated
 atom for the specific level of theory and basis set considered (and this for each element).

diff --git a/examples/submit/hortense.yaml b/examples/submit/hortense.yaml
@@ -34,7 +34,6 @@ ModelTraining:
 CP2K:
   cores_per_worker: 64
   max_evaluation_time: 30
-  memory_limit: 2GB
   launch_command: 'apptainer exec -e --no-init oras://ghcr.io/molmod/cp2k:2024.1 /opt/entry.sh mpirun -np 32 -bind-to core cp2k.psmp'
   slurm:
     partition: "cpu_rome"

diff --git a/examples/submit/lumi.yaml b/examples/submit/lumi.yaml
@@ -6,7 +6,6 @@ default_threads: 8
 CP2K:
   cores_per_worker: 32
   max_evaluation_time: 20
-  memory_limit: 2GB
   launch_command: 'singularity exec -e --no-init oras://ghcr.io/molmod/cp2k:2024.1 /opt/entry.sh mpirun -np 32 cp2k.psmp'
   slurm:
     partition: "standard"

diff --git a/pyproject.toml b/pyproject.toml
@@ -13,7 +13,7 @@ dependencies = [
     "ase >= 3.23.0",
     "pyyaml>=6.0",
     "numpy>=1.22.3,<2",
-    "parsl @ git+https://github.com/parsl/parsl.git@benc-status-refactor",
+    "parsl @ git+https://github.com/parsl/parsl.git@bugfix-default-scale-in",
     "prettytable",
     "psutil",
     "cp2k-input-tools @ git+https://github.com/cp2k/cp2k-input-tools",  # need 2024.1