diff --git a/README.md b/README.md
index df7aa1e..81641ac 100644
--- a/README.md
+++ b/README.md
@@ -8,7 +8,7 @@
 
 ## About
 
-**Ramannoodle** is a Python API for efficiently calculating Raman spectra from first principles calculations. Ramannoodle supports molecular-dynamics- and phonon-based Raman calculations and includes interfaces with VASP.
+**Ramannoodle** is a Python API for efficiently calculating Raman spectra from first principles calculations. Ramannoodle supports molecular-dynamics- and phonon-based Raman calculations. It includes interfaces with VASP but can easily be used with other codes using IO from external libraries, such as [pymatgen](https://pymatgen.org/) or [ase](https://wiki.fysik.dtu.dk/ase/).
 
 Ramannoodle aims to be:
 
@@ -24,20 +24,18 @@ Ramannoodle aims to be:
 
     Ramannoodle is designed to give the user a good understanding of what is being calculated at varying levels of abstraction.
 
-Ramannoodle includes interfaces with:
-
-* VASP
-* phonopy (planned)
-
 ## Installation
 
-Ramannoodle can be installed via pip:
+The base version of ramannoodle can be installed with pip:
 
 ```
 $ pip install ramannoodle
 ```
 
-Due to idiosyncrasies with PyTorch's build system, installing ramannoodle's machine learning modules is slightly more involved. First, PyTorch must be installed ([pip commands](https://pytorch.org/get-started/locally/)). Then, corresponding torch-scatter and torch-sparse packages must be installed. Finally, Ramannoodle can then be installed with the appropriate options.
+Ramannoodle's machine learning modules are implemented with PyTorch. To use these modules:
+1. Install [PyTorch](https://pytorch.org/get-started/locally/).
+2. Install [torch-scatter](https://pypi.org/project/torch-scatter/) and [torch-sparse](https://pypi.org/project/torch-sparse/) corresponding to the PyTorch version/implementation.
+3.  Install ramannoodle using the `torch` options group.
 
 For example, installation on a Linux system using PyTorch 2.4.1 (cpu implementation) is done as follows:
 
@@ -57,10 +55,9 @@ Contributions in the form of bug reports, feature suggestions, and pull requests
 
 ## Citing
 
-coming soon...
-
-## Future releases
+To acknowledge use of ramannoodle, please cite
 
-* **0.4.0** | ML polarizability models
-* **0.5.0** | Advanced spectra analyses
-* **1.0.0** | Official release
+>> **Rapid Characterization of Point Defects in Solid-State Ion Conductors Using Raman Spectroscopy, Machine-Learning Force Fields, and Atomic Raman Tensors** <br>
+ W. O’Leary, M. Grumet, W. Kaiser, T. Bučko, J.L.M. Rupp, D.A. Egger <br>
+ Journal of the American Chemical Society (2024) <br>
+ doi: [10.1021/jacs.4c07812](https://pubs.acs.org/doi/10.1021/jacs.4c07812)
diff --git a/docs/requirements.txt b/docs/requirements.txt
index 2c863e1..b60ae0d 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -2,4 +2,5 @@ furo==2024.8.6
 ipython==8.26.0
 nbsphinx==0.9.4
 sphinx==7.4.7
+sphinx-autodoc-typehints==2.4.4
 sphinx-rtd-theme==2.0.0
diff --git a/docs/source/conf.py b/docs/source/conf.py
index fc41684..9816893 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -26,8 +26,9 @@
     'sphinx.ext.autosummary',
     'sphinx.ext.intersphinx',
     'sphinx.ext.napoleon',
+    "sphinx_autodoc_typehints",
     'nbsphinx',
-    'IPython.sphinxext.ipython_console_highlighting'
+    'IPython.sphinxext.ipython_console_highlighting',
 ]
 autodoc_typehints = 'description'
 nbsphinx_allow_errors = True
@@ -36,6 +37,7 @@
     'python': ('https://docs.python.org/3/', None),
     'numpy': ('http://docs.scipy.org/doc/numpy', None),
     'scipy': ('http://docs.scipy.org/doc/scipy/reference', None),
+    'torch': ('https://pytorch.org/docs/stable/', None),
 }
 
 intersphinx_disabled_domains = ['std']
diff --git a/docs/source/generated/ramannoodle.dynamics.rst b/docs/source/generated/ramannoodle.dynamics.rst
index b319714..5d658b0 100644
--- a/docs/source/generated/ramannoodle.dynamics.rst
+++ b/docs/source/generated/ramannoodle.dynamics.rst
@@ -1,4 +1,4 @@
-ramannoodle.dynamics package
+dynamics
 ============================
 
 Submodules
diff --git a/docs/source/generated/ramannoodle.io.rst b/docs/source/generated/ramannoodle.io.rst
index 73f0bde..bc38ea1 100644
--- a/docs/source/generated/ramannoodle.io.rst
+++ b/docs/source/generated/ramannoodle.io.rst
@@ -1,4 +1,4 @@
-ramannoodle.io package
+io
 ======================
 
 Subpackages
diff --git a/docs/source/generated/ramannoodle.io.vasp.rst b/docs/source/generated/ramannoodle.io.vasp.rst
index 4cf5239..ef7e9c8 100644
--- a/docs/source/generated/ramannoodle.io.vasp.rst
+++ b/docs/source/generated/ramannoodle.io.vasp.rst
@@ -1,4 +1,4 @@
-ramannoodle.io.vasp package
+io.vasp
 ===========================
 
 Submodules
diff --git a/docs/source/generated/ramannoodle.polarizability.rst b/docs/source/generated/ramannoodle.polarizability.rst
index 480d8bb..2972e01 100644
--- a/docs/source/generated/ramannoodle.polarizability.rst
+++ b/docs/source/generated/ramannoodle.polarizability.rst
@@ -1,6 +1,14 @@
-ramannoodle.polarizability package
+polarizability
 ==================================
 
+Subpackages
+-----------
+
+.. toctree::
+   :maxdepth: 4
+
+   ramannoodle.polarizability.torch
+
 Submodules
 ----------
 
diff --git a/docs/source/generated/ramannoodle.polarizability.torch.rst b/docs/source/generated/ramannoodle.polarizability.torch.rst
index fefc793..e277a13 100644
--- a/docs/source/generated/ramannoodle.polarizability.torch.rst
+++ b/docs/source/generated/ramannoodle.polarizability.torch.rst
@@ -1,4 +1,4 @@
-ramannoodle.polarizability.torch package
+polarizability.torch
 ========================================
 
 Submodules
@@ -12,18 +12,18 @@ ramannoodle.polarizability.torch.dataset module
    :undoc-members:
    :show-inheritance:
 
-ramannoodle.polarizability.torch.dummy\_dataset module
-------------------------------------------------------
+ramannoodle.polarizability.torch.gnn module
+-------------------------------------------
 
-.. automodule:: ramannoodle.polarizability.torch.dummy_dataset
+.. automodule:: ramannoodle.polarizability.torch.gnn
    :members:
    :undoc-members:
    :show-inheritance:
 
-ramannoodle.polarizability.torch.gnn module
--------------------------------------------
+ramannoodle.polarizability.torch.train module
+---------------------------------------------
 
-.. automodule:: ramannoodle.polarizability.torch.gnn
+.. automodule:: ramannoodle.polarizability.torch.train
    :members:
    :undoc-members:
    :show-inheritance:
diff --git a/docs/source/generated/ramannoodle.spectrum.rst b/docs/source/generated/ramannoodle.spectrum.rst
index b86a504..c7d99e1 100644
--- a/docs/source/generated/ramannoodle.spectrum.rst
+++ b/docs/source/generated/ramannoodle.spectrum.rst
@@ -1,4 +1,4 @@
-ramannoodle.spectrum package
+spectrum
 ============================
 
 Submodules
diff --git a/docs/source/generated/ramannoodle.structure.rst b/docs/source/generated/ramannoodle.structure.rst
index 103b988..df781d9 100644
--- a/docs/source/generated/ramannoodle.structure.rst
+++ b/docs/source/generated/ramannoodle.structure.rst
@@ -1,4 +1,4 @@
-ramannoodle.structure package
+structure
 =============================
 
 Submodules
diff --git a/docs/source/introduction.rst b/docs/source/introduction.rst
index e2a47ef..847470b 100644
--- a/docs/source/introduction.rst
+++ b/docs/source/introduction.rst
@@ -3,8 +3,8 @@ Introduction
 
 A chemical system's Raman spectrum reflects the frequencies and amplitudes at which the components of its **polarizability** -- a 3x3 tensor -- fluctuate due to thermal atomic motion. We must answer two questions to calculate a Raman spectrum of a collection of atoms:
 
-1. How do the atoms "jiggle" at finite temperatures?
-2. How does each jiggle modulate polarizability?
+1. How do the atoms vibrate at finite temperatures?
+2. How does each vibration modulate polarizability?
 
 To answer question (1), we often consider the system's vibrational normal modes, i.e., phonons in the case of periodic systems. However, in cases where atomic motion is appreciably anharmonic, the phonon picture misses important features. In these cases, we use molecular dynamics to understand, at least in a statistical sense, exactly how the atoms move as a function of time.
 
@@ -17,18 +17,12 @@ Unfortunately, the need to calculate so many polarizabilities can make Raman spe
 Installation
 ------------
 
-Ramannoodle can be installed -- as is standard for Python packages -- with pip:
+Please see ramannoodle's `repo <https://github.com/wolearyc/ramannoodle>`_ for up-to-date installation instructions.
 
-.. code-block:: console
+Citing
+------
 
-      $ pip install ramannoodle
-
-So long as your Python environment is configured correctly, you should be good to go:
-
-.. code-block:: python
-
-    import ramannoodle
-    # ...
+Please see ramannoodle's `repo <https://github.com/wolearyc/ramannoodle>`_ for up-to-date citation information.
 
 Modules
 --------
diff --git a/docs/source/notebooks/machine-learning.ipynb b/docs/source/notebooks/machine-learning.ipynb
new file mode 100644
index 0000000..5cfbc6d
--- /dev/null
+++ b/docs/source/notebooks/machine-learning.ipynb
@@ -0,0 +1,489 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "81c543e7",
+   "metadata": {},
+   "source": [
+    "# Machine learning"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "388fd578",
+   "metadata": {},
+   "source": [
+    "Ramannoodle includes a brand-new machine learning model for predicting polarizabilities. This model uses a graph neural network architecture. The tutorial will demonstrate how to initialize, train, and evaluate this model. This notebook is available on [Github](https://github.com/wolearyc/ramannoodle/blob/main/docs/source/notebooks/machine-learning.ipynb).  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b8f3299-8cf3-4c00-b2b2-fb514c3aeb0f",
+   "metadata": {},
+   "source": [
+    "First, some setup."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "3956373a-63a1-4128-b0f2-e2711b5a5213",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "import matplotlib_inline\n",
+    "\n",
+    "matplotlib_inline.backend_inline.set_matplotlib_formats('png')\n",
+    "plt.rcParams['figure.dpi'] = 300\n",
+    "plt.rcParams['font.family'] = 'sans-serif'\n",
+    "plt.rcParams[\"mathtext.default\"] = 'regular'\n",
+    "plt.rcParams['axes.linewidth'] = 0.5\n",
+    "plt.rcParams['xtick.major.width'] = 0.5\n",
+    "plt.rcParams['xtick.minor.width'] = 0.5\n",
+    "plt.rcParams['lines.linewidth'] = 1.5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7da690f-f4db-4509-8c89-014e7a49c83b",
+   "metadata": {},
+   "source": [
+    "## Constructing the polarizability model\n",
+    "\n",
+    "Our final goal is to calculate TiO2's Raman spectrum. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bab6d920",
+   "metadata": {},
+   "source": [
+    "#### Datasets\n",
+    "\n",
+    "First, we will load in a training and validation set. These datasets (which are not publicly available) consist of polarizability calculations carried out on a variety of molecular dynamics snapshots. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "8e90e8f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 99/99 [00:00<00:00, 120.96 files/s]\n",
+      "100%|██████████| 100/100 [00:00<00:00, 120.69 files/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set size: 99 structures\n",
+      "Validation set size: 100 structures\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import ramannoodle.io.vasp as vasp_io\n",
+    "import glob\n",
+    "\n",
+    "# This data is not publicly available. Sorry! \n",
+    "training_set = vasp_io.outcar.read_polarizability_dataset(\n",
+    "    list(glob.glob(\"/Volumes/Untitled/TiO2_eps/train/*ps*/scratch/OUTCAR\"))\n",
+    ")\n",
+    "validation_set = vasp_io.outcar.read_polarizability_dataset(\n",
+    "    list(glob.glob(\"/Volumes/Untitled/TiO2_eps/validation/*ps*/scratch/OUTCAR\"))\n",
+    ")\n",
+    "# As is best practice, we scale the validation set with respect to the training set.\n",
+    "validation_set.scale_polarizabilities(\n",
+    "    training_set.mean_polarizability, training_set.stddev_polarizability\n",
+    ")\n",
+    "\n",
+    "print(\"Training set size:\", len(training_set), \"structures\")\n",
+    "print(\"Validation set size:\", len(validation_set), \"structures\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1655c4c0",
+   "metadata": {},
+   "source": [
+    "Let's plot the training set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "373b782a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2400x600 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from torch.utils.data import DataLoader\n",
+    "\n",
+    "fig = plt.figure(constrained_layout=True, figsize=(8, 2))\n",
+    "gs = fig.add_gridspec(1, 5, wspace=0)\n",
+    "axes = gs.subplots()\n",
+    "loader = DataLoader(training_set, batch_size = 100)\n",
+    "\n",
+    "subscripts = [\"xx\",\"yy\",\"zz\",\"xy\",\"xz\",\"yz\"]\n",
+    "for lattice, atomic_numbers, positions, true_polarizability  in loader:\n",
+    "    for i in range(5): \n",
+    "        axis = axes[i]  # type: ignore\n",
+    "        axis.scatter(true_polarizability[:,i+1].cpu(), true_polarizability[:,0].cpu(), color=\"black\")\n",
+    "        axis.set_xlabel(r\"$\\alpha_{\" + subscripts[i+1] + \"}$\")\n",
+    "        if i == 0:\n",
+    "            axis.set_ylabel(r\"$\\alpha_{\" + subscripts[0] + \"}$\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ac3cc94",
+   "metadata": {},
+   "source": [
+    "Note that the dataset internally standard-scales each independent element of the polarizability tensor. \n",
+    "\n",
+    "### Model initialization\n",
+    "\n",
+    "With the dataset loaded, we can initialize the model by specifying a reference structure, several hyperparameters, as well as the mean/standardization used to scale the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "9d51a6e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from ramannoodle.polarizability.torch.gnn import PotGNN\n",
+    "ref_structure = vasp_io.poscar.read_ref_structure(\"../../../test/data/TiO2/POSCAR\")\n",
+    "\n",
+    "model = PotGNN(\n",
+    "    ref_structure,\n",
+    "    size_node_embedding=5,\n",
+    "    size_edge_embedding=14,\n",
+    "    num_message_passes=4,\n",
+    "    cutoff = 2,\n",
+    "    gaussian_filter_start=0,\n",
+    "    gaussian_filter_end=5,\n",
+    "    mean_polarizability=training_set.mean_polarizability,\n",
+    "    stddev_polarizability=training_set.stddev_polarizability\n",
+    ")\n",
+    "\n",
+    "# Recommended initialization for weights and biases.\n",
+    "def init_biases(m):\n",
+    "    if isinstance(m,torch.nn.Linear):\n",
+    "        torch.nn.init.uniform_(m.bias,-0.5,0.5)\n",
+    "def init_weights(m):\n",
+    "    if isinstance(m,torch.nn.Linear) or isinstance(m,torch.nn.Embedding):\n",
+    "        torch.nn.init.normal_(m.weight, mean = 0, std = 1)\n",
+    "\n",
+    "model = model.apply(init_biases)\n",
+    "model = model.apply(init_weights)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08a824c8",
+   "metadata": {},
+   "source": [
+    "In the first stage of training, we use a fairly large learning rate and a relatively small batch size. During this training, we expect the training loss to decrease fairly nicely. Validation loss will be extremely noisy (due to batch normalization within the model), while the variance of the validation predictions will approach 1 for each polarizability component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "22482cbb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 85%|████████▌ | 85/100 [01:09<00:12,  1.22it/s, training_loss=0.147, validation_loss=106, validation_var=[1.31 0.6  0.33 1.49 0.81 0.7 ]]                        \n"
+     ]
+    }
+   ],
+   "source": [
+    "from ramannoodle.polarizability.torch.train import train_single_epoch\n",
+    "\n",
+    "loss_function = torch.nn.MSELoss()\n",
+    "optimizer = torch.optim.Adam(model.parameters(), lr=0.02)\n",
+    "\n",
+    "from tqdm import trange\n",
+    "with trange(100) as t:\n",
+    "    for epoch in t:\n",
+    "        training_loss, validation_loss, validation_var = train_single_epoch(\n",
+    "            model, training_set, validation_set, 5, optimizer, loss_function\n",
+    "        )\n",
+    "        t.set_postfix(\n",
+    "            training_loss=training_loss, \n",
+    "            validation_loss=validation_loss, \n",
+    "            validation_var=np.array2string(validation_var, precision = 2)\n",
+    "        )\n",
+    "        if training_loss < 0.15:\n",
+    "            break \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab4ae08b",
+   "metadata": {},
+   "source": [
+    "We halt training once the training loss goes below 0.150. However, the validation loss is extremely high. In the second stage of training, we turn down the learning rate to stabilize the batch normalization term (and therefore stabilize the validation loss)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "c150f319",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 24%|██▍       | 24/100 [00:17<00:56,  1.34it/s, training_loss=0.124, validation_loss=0.164, validation_var=[0.78 1.16 0.68 0.77 1.17 0.83]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from ramannoodle.polarizability.torch.train import train_single_epoch\n",
+    "\n",
+    "loss_function = torch.nn.MSELoss()\n",
+    "optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)\n",
+    "\n",
+    "from tqdm import trange\n",
+    "with trange(100) as t:\n",
+    "    for epoch in t:\n",
+    "        training_loss, validation_loss, validation_var = train_single_epoch(\n",
+    "            model, training_set, validation_set, 5, optimizer, loss_function\n",
+    "        )\n",
+    "        t.set_postfix(\n",
+    "            training_loss=training_loss, \n",
+    "            validation_loss=validation_loss, \n",
+    "            validation_var=np.array2string(validation_var, precision = 2)\n",
+    "        )\n",
+    "        if validation_loss < 0.165:\n",
+    "            break \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc61963f",
+   "metadata": {},
+   "source": [
+    "Now that the model has been roughly trained, we can visualize it's performance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "d17d3dd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3600x600 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import torcheval\n",
+    "import torcheval.metrics\n",
+    "\n",
+    "fig = plt.figure(constrained_layout=True, figsize=(12, 2))\n",
+    "gs = fig.add_gridspec(1, 6, wspace=0)\n",
+    "axes = gs.subplots()\n",
+    "plot_train_loader = DataLoader(training_set, batch_size = 100)\n",
+    "plot_validation_loader = DataLoader(validation_set, batch_size = 100)\n",
+    "\n",
+    "model.eval()\n",
+    "\n",
+    "subscripts = [\"xx\",\"yy\",\"zz\",\"xy\",\"xz\",\"yz\"]\n",
+    "for i in range(6): \n",
+    "    axis = axes[i]  # type: ignore\n",
+    "    for lattice, atomic_numbers, positions, true_polarizability  in plot_train_loader:\n",
+    "        predicted_polarizability = model(lattice, atomic_numbers, positions).detach()\n",
+    "        loss = loss_function(true_polarizability[:,i], predicted_polarizability[:,i])\n",
+    "        metric = torcheval.metrics.R2Score()\n",
+    "        metric.update(predicted_polarizability[:,i], true_polarizability[:,i])\n",
+    "        R2 = metric.compute()\n",
+    "        axis.scatter(\n",
+    "            true_polarizability[:,i], predicted_polarizability[:,i], color=\"black\", \n",
+    "            label = f\"Training, R2 = {R2:.3f}\"\n",
+    "        )\n",
+    "    for lattice, atomic_numbers, positions, true_polarizability  in plot_validation_loader:\n",
+    "        predicted_polarizability = model(lattice, atomic_numbers, positions).detach()\n",
+    "        loss = loss_function(true_polarizability[:,i], predicted_polarizability[:,i])\n",
+    "        metric = torcheval.metrics.R2Score()\n",
+    "        metric.update(predicted_polarizability[:,i], true_polarizability[:,i])\n",
+    "        R2 = metric.compute()\n",
+    "        axis.scatter(\n",
+    "            true_polarizability[:,i], predicted_polarizability[:,i], color=\"red\",\n",
+    "            label = f\"Validation, R2 = {R2:.3f}\"\n",
+    "        )\n",
+    "        \n",
+    "    axis.plot([-2.5,2.5],[-2.5,2.5],color = \"blue\", zorder = 5)\n",
+    "    axis.set_xlabel(r\"True $\\alpha_{\" + subscripts[i] + \"}$\")\n",
+    "    axis.set_ylabel(r\"Predicted $\\alpha_{\" + subscripts[i] + \"}$\")\n",
+    "    l = axis.legend(fontsize =\"xx-small\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1649a50d",
+   "metadata": {},
+   "source": [
+    "The model is performing fairly well, though it does struggle somewhat on the zz and xy components. With the model trained, we can use it to compute a Raman spectrum."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ea2fec2",
+   "metadata": {},
+   "source": [
+    "### Raman spectrum calculation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "533bd606",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ramannoodle.dynamics.trajectory import Trajectory\n",
+    "import ramannoodle.io.vasp as vasp_io\n",
+    "\n",
+    "# This trajectory is not publicly available. Sorry! \n",
+    "trajectory = vasp_io.vasprun.read_trajectory(\n",
+    "    f\"/Volumes/Untitled/md/TiO2/production.xml\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "b1f2cb2c-8444-4b6a-ae8e-f1ac6c6a6e98",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 20000/20000 [00:35<00:00, 568.61 configs/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Compute spectrum\n",
+    "spectrum = trajectory.get_raman_spectrum(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "7d182420",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 2400x900 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from ramannoodle.spectrum.spectrum_utils import convolve_spectrum\n",
+    "\n",
+    "# Then plot\n",
+    "wavenumbers, total_intensities = spectrum.measure(laser_correction = True, \n",
+    "                                           laser_wavelength = 532, \n",
+    "                                           bose_einstein_correction = True, \n",
+    "                                           temperature = 300)\n",
+    "\n",
+    "fig = plt.figure(constrained_layout = True, figsize = (8, 3))\n",
+    "axis = fig.add_subplot(111)\n",
+    "lower_cutoff = 50 \n",
+    "axis.plot(\n",
+    "    wavenumbers[wavenumbers > lower_cutoff], \n",
+    "    total_intensities[wavenumbers > lower_cutoff] / np.max(total_intensities[wavenumbers > lower_cutoff]), \n",
+    "    label = \"raw\", color = 'black', linewidth = 0.5\n",
+    ")\n",
+    "wavenumbers, total_intensities = convolve_spectrum(wavenumbers, total_intensities)\n",
+    "axis.plot(\n",
+    "    wavenumbers[wavenumbers > lower_cutoff], \n",
+    "    total_intensities[wavenumbers > lower_cutoff] / np.max(total_intensities[wavenumbers > lower_cutoff]), \n",
+    "    label = \"smoothed\", color = \"red\"\n",
+    ")\n",
+    "\n",
+    "axis.set_xlim((0,1000))\n",
+    "axis.legend()\n",
+    "axis.set_ylabel(\"Intensity (a.u.)\")\n",
+    "l = axis.set_xlabel(r\"Raman shift ($\\mathregular{cm^{-1}}$)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0686c41",
+   "metadata": {},
+   "source": [
+    "This is a excellent spectrum that closely resembles experimental data."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/tutorials.rst b/docs/source/tutorials.rst
index a81e78f..d7292e5 100644
--- a/docs/source/tutorials.rst
+++ b/docs/source/tutorials.rst
@@ -9,3 +9,4 @@ Tutorials
    notebooks/full-workflow
    notebooks/molecular-dynamics
    notebooks/masking
+   notebooks/machine-learning
diff --git a/ramannoodle/dynamics/abstract.py b/ramannoodle/dynamics/abstract.py
index a738196..16096b4 100644
--- a/ramannoodle/dynamics/abstract.py
+++ b/ramannoodle/dynamics/abstract.py
@@ -18,5 +18,5 @@ def get_raman_spectrum(
         Parameters
         ----------
         polarizability_model
-            | Must be compatible with the dynamics.
+            Must be compatible with the dynamics.
         """
diff --git a/ramannoodle/dynamics/phonon.py b/ramannoodle/dynamics/phonon.py
index 2ef70fa..cf5ce44 100644
--- a/ramannoodle/dynamics/phonon.py
+++ b/ramannoodle/dynamics/phonon.py
@@ -1,4 +1,4 @@
-"""Harmonic lattice vibrations aka phonons."""
+"""Harmonic lattice vibrations."""
 
 import numpy as np
 from numpy.typing import NDArray
@@ -21,11 +21,11 @@ class Phonons(Dynamics):
     Parameters
     ----------
     ref_positions
-        | (fractional) 2D array with shape (N,3) where N is the number of atoms.
+        (fractional) Array with shape (N,3) where N is the number of atoms.
     wavenumbers
-        | (cm\ :sup:`-1`) 1D array with shape (M,).
+        (cm\ :sup:`-1`) Array with shape (M,).
     displacements
-        | (fractional) 3D array with shape (M,N,3).
+        (fractional) Array with shape (M,N,3).
     """
 
     def __init__(
@@ -52,7 +52,7 @@ def ref_positions(self) -> NDArray[np.float64]:
         Returns
         -------
         :
-            (fractional) 2D array with shape (N,3) where N is the number of atoms.
+            (fractional) Array with shape (N,3) where N is the number of atoms.
         """
         return self._ref_positions.copy()
 
@@ -63,7 +63,7 @@ def wavenumbers(self) -> NDArray[np.float64]:
         Returns
         -------
         :
-            (cm\ :sup:`-1`) 1D array with shape (M,) where M is the number of phonons.
+            (cm\ :sup:`-1`) Array with shape (M,) where M is the number of phonons.
         """
         return self._wavenumbers.copy()
 
@@ -74,7 +74,7 @@ def displacements(self) -> NDArray[np.float64]:
         Returns
         -------
         :
-            (fractional) 3D array with shape (M,N,3) where M is the number of phonons
+            (fractional) Array with shape (M,N,3) where M is the number of phonons
             and N is the number of atoms.
         """
         return self._displacements.copy()
@@ -87,7 +87,7 @@ def get_raman_spectrum(
         Parameters
         ----------
         polarizability_model
-            | Must be compatible with phonons.
+            Must be compatible with phonons.
         """
         raman_tensors = []
         for displacement in self._displacements:
diff --git a/ramannoodle/dynamics/trajectory.py b/ramannoodle/dynamics/trajectory.py
index a83611c..c1e81c8 100644
--- a/ramannoodle/dynamics/trajectory.py
+++ b/ramannoodle/dynamics/trajectory.py
@@ -1,4 +1,4 @@
-"""Molecular dynamics trajectories."""
+"""Molecular dynamics trajectory."""
 
 from collections.abc import Sequence
 from typing import overload
@@ -22,10 +22,10 @@ class Trajectory(Dynamics, Sequence[NDArray[np.float64]]):
     Parameters
     ----------
     positions_ts
-        | (fractional) 3D array with shape (S,N,3) where S in the number of
-        | configurations and N is the number of atoms.
+        (fractional) Array with shape (S,N,3) where S in the number of
+        configurations and N is the number of atoms.
     timestep
-        | (fs)
+        (fs)
 
     """
 
@@ -52,14 +52,20 @@ def positions_ts(self) -> NDArray[np.float64]:
         Returns
         -------
         :
-            (fractional) 3D array with shape (S,N,3) where S in the number of
+            (fractional) Array with shape (S,N,3) where S in the number of
             configurations and N is the number of atoms.
         """
         return self._positions_ts.copy()
 
     @property
     def timestep(self) -> float:
-        """Get timestep in fs."""
+        """Get timestep.
+
+        Returns
+        -------
+        :
+            (fs)
+        """
         return self._timestep
 
     def get_raman_spectrum(
@@ -70,7 +76,7 @@ def get_raman_spectrum(
         Parameters
         ----------
         polarizability_model
-            | Must be compatible with the trajectory.
+            Must be compatible with the trajectory.
         """
         try:
             polarizability_ts = polarizability_model.calc_polarizabilities(
diff --git a/ramannoodle/exceptions.py b/ramannoodle/exceptions.py
index 1047382..e955f4c 100644
--- a/ramannoodle/exceptions.py
+++ b/ramannoodle/exceptions.py
@@ -10,11 +10,11 @@ class NoMatchingLineFoundException(Exception):
 
 
 class InvalidFileException(Exception):
-    """File cannot be read, likely due to due to invalid or unexpected format."""
+    """File cannot not be read, likely due to due to invalid or unexpected format."""
 
 
 class IncompatibleStructureException(Exception):
-    """Supplied file is incompatible."""
+    """File contains structure that is incompatible with the current operation."""
 
 
 class InvalidDOFException(Exception):
@@ -41,8 +41,7 @@ class UserError(Exception):
 def _shape_string(shape: Sequence[int | None]) -> str:
     """Get a string representing a shape.
 
-    Maps None --> "_", indicating that this element can
-    be anything.
+    Maps None --> "_", indicating that this element can be anything.
     """
     result = "("
     for i in shape:
@@ -109,7 +108,7 @@ def verify_ndarray_shape(
 def verify_list_len(name: str, array: list[Any], length: int | None) -> None:
     """Verify an list's shape.
 
-    We should avoid calling this function whenever possible (EATF).
+    Calling function should be avoided whenever possible (EATF).
 
     :meta private:
 
diff --git a/ramannoodle/io/generic.py b/ramannoodle/io/generic.py
index b42bdc8..2ec45ea 100644
--- a/ramannoodle/io/generic.py
+++ b/ramannoodle/io/generic.py
@@ -3,7 +3,7 @@
 Generic IO functions are somewhat inflexible but are necessary for certain
 functionality. Users are strongly encouraged to use IO functions contained in the
 code-specific subpackages. For example, IO for VASP POSCAR and OUTCAR files can be
-accomplished using :mod:`ramannoodle.io.vasp.poscar` or
+accomplished using :mod:`ramannoodle.io.vasp.poscar` and
 :mod:`ramannoodle.io.vasp.outcar` respectively.
 
 """
@@ -72,7 +72,7 @@ def read_phonons(filepath: str | Path, file_format: str) -> Phonons:
     ----------
     filepath
     file_format
-        | Supports ``"outcar"``, ``"vasprun.xml"`` (see :ref:`Supported formats`).
+        Supports ``"outcar"``, ``"vasprun.xml"`` (see :ref:`Supported formats`).
 
     Returns
     -------
@@ -98,9 +98,9 @@ def read_trajectory(filepath: str | Path, file_format: str) -> Trajectory:
     ----------
     filepath
     file_format
-        | Supports ``"outcar"``, ``"vasprun.xml"``, (see :ref:`Supported formats`).
-        | Use :func:`.vasp.xdatcar.read_trajectory` to read a trajectory from an
-        | XDATCAR.
+        Supports ``"outcar"``, ``"vasprun.xml"``, (see :ref:`Supported formats`).
+        Use :func:`.vasp.xdatcar.read_trajectory` to read a trajectory from an
+        XDATCAR.
 
     Returns
     -------
@@ -133,16 +133,14 @@ def read_positions_and_polarizability(
     ----------
     filepath
     file_format
-        | Supports ``"outcar"``, ``"vasprun.xml"`` (see :ref:`Supported formats`).
+        Supports ``"outcar"``, ``"vasprun.xml"`` (see :ref:`Supported formats`).
 
     Returns
     -------
     :
-        2-tuple:
-            0. | positions --
-               | (fractional) 2D array with shape (N,3) where N is the number of atoms.
-            #. | polarizability --
-               | (fractional) 2D array with shape (3,3).
+        0.  positions -- (fractional) Array with shape (N,3) where N is the number of
+            atoms.
+        #.  polarizability -- Array with shape (3,3).
 
     Raises
     ------
@@ -167,23 +165,23 @@ def read_structure_and_polarizability(
     ----------
     filepath
     file_format
-        Supports: ``"outcar"``, ``"vasprun.xml"`` (see :ref:`Supported formats`)
+        Supports ``"outcar"``, ``"vasprun.xml"`` (see :ref:`Supported formats`)
 
     Returns
     -------
     :
-        4-tuple, whose first element is the lattice (Å), a 2D array with shape (3,3).
-        The second element is the atomic numbers, a list of length N where N is the
-        number of atoms. The third element is positions, a 2D array with shape (N,3).
-        The fourth element is the polarizability (unitless), a 2D array with shape
-        (3,3).
+        0.  lattice -- (Å) Array with shape (3,3).
+        #.  atomic_numbers -- List of length N where N is the number of atoms.
+        #.  positions -- (fractional) Array with shape (N,3) where N is the number of
+            atoms.
+        #.  polarizability -- Array with shape (3,3).
 
     Raises
     ------
-    InvalidFileException
-        File has unexpected format.
     FileNotFoundError
-        File could not be found.
+        File not found.
+    InvalidFileException
+        Invalid file.
     """
     try:
         return _STRUCTURE_AND_POLARIZABILITY_READERS[file_format](filepath)
@@ -199,9 +197,9 @@ def read_polarizability_dataset(
 
     Parameters
     ----------
-    filepath
+    filepaths
     file_format
-        | Supports ``"outcar"``, ``"vasprun.xml"`` (see :ref:`Supported formats`)
+        Supports ``"outcar"``, ``"vasprun.xml"`` (see :ref:`Supported formats`)
 
     Returns
     -------
@@ -210,8 +208,9 @@ def read_polarizability_dataset(
     Raises
     ------
     FileNotFoundError
+        File not found.
     InvalidFileException
-        File has an unexpected format.
+        Invalid file.
     IncompatibleFileException
         File is incompatible with the dataset.
     """
@@ -233,13 +232,13 @@ def read_positions(
     ----------
     filepath
     file_format
-        | Supports ``"outcar"``, ``"poscar"``, ``"xdatcar"``, ``"vasprun.xml"``  (see
-        | :ref:`Supported formats`).
+        Supports ``"outcar"``, ``"poscar"``, ``"xdatcar"``, ``"vasprun.xml"``  (see
+        :ref:`Supported formats`).
 
     Returns
     -------
     :
-        Unitless | 2D array with shape (N,3) where N is the number of atoms.
+        (fractional) Array with shape (N,3) where N is the number of atoms.
 
     Raises
     ------
@@ -262,8 +261,8 @@ def read_ref_structure(filepath: str | Path, file_format: str) -> ReferenceStruc
     ----------
     filepath
     file_format
-        | Supports ``"outcar"``, ``"poscar"``, ``"xdatcar"``, ``"vasprun.xml"`` (see
-        | :ref:`Supported formats`).
+        Supports ``"outcar"``, ``"poscar"``, ``"xdatcar"``, ``"vasprun.xml"`` (see
+        :ref:`Supported formats`).
 
     Returns
     -------
@@ -297,18 +296,18 @@ def write_structure(  # pylint: disable=too-many-arguments
     Parameters
     ----------
     lattice
-        | (Å) 2D array with shape (3,3).
+        (Å) Array with shape (3,3).
     atomic_numbers
-        | 1D list of length N where N is the number of atoms.
+        List of length N where N is the number of atoms.
     positions
-        | (fractional) 2D array with shape (N,3).
+        (fractional) Array with shape (N,3).
     filepath
     file_format
-        | Supports ``"poscar"`` (see :ref:`Supported formats`).
+        Supports ``"poscar"`` (see :ref:`Supported formats`).
     overwrite
-        | Overwrite the file if it exists.
+        Overwrite the file if it exists.
     label
-        | POSCAR label (first line).
+        POSCAR label (first line).
 
     Raises
     ------
@@ -340,17 +339,17 @@ def write_trajectory(  # pylint: disable=too-many-arguments
     Parameters
     ----------
     lattice
-        | (Å) 2D array with shape (3,3).
+        (Å) Array with shape (3,3).
     atomic_numbers
-        | 1D list of length N where N is the number of atoms.
+        List of length N where N is the number of atoms.
     positions_ts
-        | (fractional) 3D array with shape (S,N,3) where S is the number of
-        | configurations.
+        (fractional) Array with shape (S,N,3) where S is the number of
+        configurations.
     filepath
     file_format
-        | Supports ``"xdatcar"`` (see :ref:`Supported formats`).
+        Supports ``"xdatcar"`` (see :ref:`Supported formats`).
     overwrite
-        | Overwrite the file if it exists.
+        Overwrite the file if it exists.
 
     Raises
     ------
diff --git a/ramannoodle/io/io_utils.py b/ramannoodle/io/io_utils.py
index d0d11cc..abbc40a 100644
--- a/ramannoodle/io/io_utils.py
+++ b/ramannoodle/io/io_utils.py
@@ -118,7 +118,7 @@ def _read_polarizability_dataset(
     ------
     FileNotFoundError
     InvalidFileException
-        File has an unexpected format.
+        Invalid file.
     IncompatibleFileException
         File is incompatible with the dataset.
     """
diff --git a/ramannoodle/io/vasp/outcar.py b/ramannoodle/io/vasp/outcar.py
index 2a3258e..a281737 100644
--- a/ramannoodle/io/vasp/outcar.py
+++ b/ramannoodle/io/vasp/outcar.py
@@ -318,11 +318,9 @@ def read_positions_and_polarizability(
     Returns
     -------
     :
-        2-tuple:
-            0. | positions --
-               | (fractional) 2D array with shape (N,3) where N is the number of atoms.
-            #. | polarizability --
-               | (fractional) 2D array with shape (3,3).
+        0.  positions -- (fractional) Array with shape (N,3) where N is the number of
+            atoms.
+        #.  polarizability -- Array with shape (3,3).
 
     Raises
     ------
@@ -349,7 +347,7 @@ def read_positions(filepath: str | Path) -> NDArray[np.float64]:
     Returns
     -------
     :
-        (fractional) 2D array with shape (N,3) where N is the number of atoms.
+        (fractional) Array with shape (N,3) where N is the number of atoms.
 
     Raises
     ------
@@ -380,17 +378,18 @@ def read_structure_and_polarizability(
     Returns
     -------
     :
-        4-tuple, whose first element is the lattice (Å), a 2D array with shape (3,3).
-        The second element is the atomic numbers, a list of length N where N is the
-        number of atoms. The third element is positions, a 2D array with shape (N,3).
-        The fourth element is the polarizability (unitless), a 2D array with shape
-        (3,3).
+        0.  lattice -- (Å) Array with shape (3,3).
+        #.  atomic_numbers -- List of length N where N is the number of atoms.
+        #.  positions -- (fractional) Array with shape (N,3) where N is the number of
+            atoms.
+        #.  polarizability -- Array with shape (3,3).
 
     Raises
     ------
     FileNotFoundError
+        File not found.
     InvalidFileException
-        File has an unexpected format.
+        Invalid file.
     """
     filepath = pathify(filepath)
     with open(filepath, "r", encoding="utf-8") as outcar_file:
@@ -418,8 +417,9 @@ def read_polarizability_dataset(
     Raises
     ------
     FileNotFoundError
+        File not found.
     InvalidFileException
-        File has an unexpected format.
+        Invalid file.
     IncompatibleFileException
         File is incompatible with the dataset.
     """
diff --git a/ramannoodle/io/vasp/poscar.py b/ramannoodle/io/vasp/poscar.py
index 23d2841..2a0cfdc 100644
--- a/ramannoodle/io/vasp/poscar.py
+++ b/ramannoodle/io/vasp/poscar.py
@@ -132,7 +132,7 @@ def read_positions(
     Returns
     -------
     :
-        (fractional) 2D array with shape (N,3) where N is the number of atoms.
+        (fractional) Array with shape (N,3) where N is the number of atoms.
 
     Raises
     ------
@@ -223,16 +223,16 @@ def write_structure(  # pylint: disable=too-many-arguments
     Parameters
     ----------
     lattice
-        | (Å) 2D array with shape (3,3).
+        (Å) Array with shape (3,3).
     atomic_numbers
-        | 1D list of length N where N is the number of atoms.
+        List of length N where N is the number of atoms.
     positions
-        | (fractional) 2D array with shape (N,3).
+        (fractional) Array with shape (N,3).
     filepath
     overwrite
-        | Overwrite the file if it exists.
+        Overwrite the file if it exists.
     label
-        | POSCAR label (first line).
+        POSCAR label (first line).
     """
     verify_structure(lattice, atomic_numbers, positions)
     filepath = pathify(filepath)
diff --git a/ramannoodle/io/vasp/vasprun.py b/ramannoodle/io/vasp/vasprun.py
index 3a85ae2..a0d76a3 100644
--- a/ramannoodle/io/vasp/vasprun.py
+++ b/ramannoodle/io/vasp/vasprun.py
@@ -128,11 +128,9 @@ def read_positions_and_polarizability(
     Returns
     -------
     :
-        2-tuple:
-            0. | positions --
-               | (fractional) 2D array with shape (N,3) where N is the number of atoms.
-            #. | polarizability --
-               | (fractional) 2D array with shape (3,3).
+        0.  positions -- (fractional) Array with shape (N,3) where N is the number of
+            atoms.
+        1.  polarizability -- Array with shape (3,3).
 
     Raises
     ------
@@ -167,19 +165,16 @@ def read_structure_and_polarizability(
     Returns
     -------
     :
-        4-tuple:
-            0. | lattice --
-               | (Å) 2D array with shape (3,3).
-            #. | atomic numbers --
-               | List of length N where N is the number of atoms.
-            #. | positions --
-               | (fractional) 2D array with shape (N,3) where N is the number of atoms.
-            #. | polarizability --
-               | 2D array with shape (3,3).
+        0.  lattice -- (Å) Array with shape (3,3).
+        1.  atomic numbers -- List of length N where N is the number of atoms.
+        2.  positions -- (fractional) Array with shape (N,3) where N is the number of
+            atoms.
+        3.  polarizability -- Array with shape (3,3).
 
     Raises
     ------
     FileNotFoundError
+        File not found.
     InvalidFileException
         Invalid file.
     """
@@ -213,8 +208,9 @@ def read_polarizability_dataset(
     Raises
     ------
     FileNotFoundError
+        File not found.
     InvalidFileException
-        File has an unexpected format.
+        Invalid file.
     IncompatibleFileException
         File is incompatible with the dataset.
     """
@@ -231,7 +227,7 @@ def read_positions(filepath: str | Path) -> NDArray[np.float64]:
     Returns
     -------
     :
-        (fractional) 2D array with shape (N,3) where N is the number of atoms.
+        (fractional) Array with shape (N,3) where N is the number of atoms.
 
     Raises
     ------
diff --git a/ramannoodle/io/vasp/xdatcar.py b/ramannoodle/io/vasp/xdatcar.py
index a2e097f..35360d9 100644
--- a/ramannoodle/io/vasp/xdatcar.py
+++ b/ramannoodle/io/vasp/xdatcar.py
@@ -30,7 +30,7 @@ def read_positions_ts(
     Returns
     -------
     :
-        (fractional) 2D array with shape (S,N,3) where S is the number of configurations
+        (fractional) Array with shape (S,N,3) where S is the number of configurations
         and N is the number of atoms.
 
     Raises
@@ -68,7 +68,7 @@ def read_trajectory(
     ----------
     filepath
     timestep
-        | (fs)
+        (fs)
 
     Raises
     ------
@@ -94,17 +94,17 @@ def write_trajectory(  # pylint: disable=too-many-arguments
     Parameters
     ----------
     lattice
-        | (Å) 2D array with shape (3,3).
+        (Å) Array with shape (3,3).
     atomic_numbers
-        | 1D list of length N where N is the number of atoms.
+        List of length N where N is the number of atoms.
     positions_ts
-        | (fractional) 3D array with shape (S,N,3) where S is the number of
-        | configurations.
+        (fractional) Array with shape (S,N,3) where S is the number of
+        configurations.
     filepath
     overwrite
-        | Overwrite the file if it exists.
+        Overwrite the file if it exists.
     label
-        | XDATCAR label (first line).
+        XDATCAR label (first line).
     """
     verify_trajectory(lattice, atomic_numbers, positions_ts)
     filepath = pathify(filepath)
diff --git a/ramannoodle/polarizability/abstract.py b/ramannoodle/polarizability/abstract.py
index c6454bd..3e4dd31 100644
--- a/ramannoodle/polarizability/abstract.py
+++ b/ramannoodle/polarizability/abstract.py
@@ -1,4 +1,4 @@
-"""Abstract polarizability models."""
+"""Abstract polarizability model."""
 
 from abc import ABC, abstractmethod
 
@@ -18,11 +18,11 @@ def calc_polarizabilities(
         Parameters
         ----------
         positions_batch
-            | (fractional) 3D array with shape (S,N,3) where S is the number of samples
-            | and N is the number of atoms.
+            (fractional) Array with shape (S,N,3) where S is the number of samples
+            and N is the number of atoms.
 
         Returns
         -------
         :
-            3D array with shape (S,3,3).
+            Array with shape (S,3,3).
         """
diff --git a/ramannoodle/polarizability/art.py b/ramannoodle/polarizability/art.py
index 9005d1b..5ec56d6 100644
--- a/ramannoodle/polarizability/art.py
+++ b/ramannoodle/polarizability/art.py
@@ -54,7 +54,7 @@ class ARTModel(InterpolationModel):
         Degrees of freedom cannot (and should not) be added using the :meth:`.add_dof`
         and :meth:`.add_dof_from_files` methods inherited from
         :class:`.InterpolationModel`. Usage of these methods will raise a
-        :class:`.UsageError`. Instead, use :meth:`add_art` or
+        :class:`.UserError`. Instead, use :meth:`add_art` or
         :meth:`add_art_from_files`.
 
     .. warning::
@@ -65,9 +65,9 @@ class ARTModel(InterpolationModel):
     Parameters
     ----------
     ref_structure
-        | Reference structure on which to base the model.
+        Reference structure on which to base the model.
     ref_polarizability
-        | 2D array with shape (3,3) giving polarizability of the reference structure.
+        Array with shape (3,3) giving polarizability of the reference structure.
     is_dummy_model
 
     """
@@ -84,7 +84,7 @@ def add_dof(  # pylint: disable=too-many-arguments
 
         Raises
         ------
-        UsageError
+        UserError
 
         :meta private:
         """
@@ -100,7 +100,7 @@ def add_dof_from_files(
 
         Raises
         ------
-        UsageError
+        UserError
 
         :meta private:
         """
@@ -125,22 +125,18 @@ def add_art(
         Parameters
         ----------
         atom_index
-            | Atom index in the reference structure.
+            Atom index in the reference structure.
         cart_direction
-            (Å) 1D array with shape (3,).
-
-            Must be orthogonal to all previously added ARTs belonging to the atom at
-            ``atom_index``.
+            (Å) Array with shape (3,). Must be orthogonal to all previously added
+            ARTs belonging to the atom at ``atom_index``.
         amplitudes
-            (Å) 1D array with shape (1,) or (2,).
-
-            Duplicate amplitudes, either those explicitly provided or those generated
-            by structural symmetries, will raise :class:`.InvalidDOFException`.
+            (Å) Array with shape (1,) or (2,). Duplicate amplitudes, either those
+            explicitly provided or those generated by structural symmetries, will
+            raise :class:`.InvalidDOFException`.
         polarizabilities
-            3D array with shape (1,3,3) or (2,3,3) containing known
-            polarizabilities for each amplitude.
-
-            If dummy model, this parameter is ignored.
+            Array with shape (1,3,3) or (2,3,3) containing known
+            polarizabilities for each amplitude. If dummy model, this parameter is
+            ignored.
 
         Raises
         ------
@@ -189,9 +185,10 @@ def add_art_from_files(
         ----------
         filepaths
         file_format
-            Supports ``"outcar"`` and ``"vasprun.xml"`` (see :ref:`Supported formats`).
+            Supports ``"outcar"`` and ``"vasprun.xml"``. If dummy model, supports
+            ``"poscar"`` and ``"xdatcar"`` as well (see :ref:`Supported formats`).
+
 
-            If dummy model, supports ``"poscar"`` and ``"xdatcar"`` as well.
 
         Raises
         ------
@@ -238,12 +235,12 @@ def get_specification_tuples(
         Returns
         -------
         :
-            List of 3-tuples:
-                0. An atom index, referred to below as ``parent_atom_index``
-                1. List of atom indexes that are symmetrically equivalent to
-                   ``parent_atom_index``
-                #. | Currently specified ART directions for ``parent_atom_index``
-                   | (Å) List of 1D arrays with shape (3,).
+
+            0.  parent atom index
+            #.  List of atom indexes that are symmetrically equivalent to
+                parent atom index
+            #.  Currently specified ART directions for parent atom indexes -- (Å) List
+                of arrays with shape (3,).
 
         """
         equivalent_atom_dict = self._ref_structure.get_equivalent_atom_dict()
@@ -268,9 +265,8 @@ def get_dof_indexes(
         ----------
         atom_indexes_or_symbols
             If integer or list of integers, specifies atom indexes. If string or list
-            of strings, specifies atom symbols.
-
-            Mixtures of indexes and symbols are allowed.
+            of strings, specifies atom symbols. Mixtures of indexes and symbols are
+            allowed.
 
         """
         if not isinstance(atom_indexes_or_symbols, list):
@@ -337,8 +333,8 @@ def get_masked_model(self, dof_indexes_to_mask: list[int]) -> ARTModel:
         Parameters
         ----------
         dof_indexes_to_mask
-            | DOF indexes associated with specific atoms can be retrieved using
-            | :meth:`get_dof_indexes`.
+            DOF indexes associated with specific atoms can be retrieved using
+            :meth:`get_dof_indexes`.
         """
         # We "cast" here due to how typing is done to support Python 3.10.
         return cast(ARTModel, super().get_masked_model(dof_indexes_to_mask))
diff --git a/ramannoodle/polarizability/interpolation.py b/ramannoodle/polarizability/interpolation.py
index 92d2248..289370d 100644
--- a/ramannoodle/polarizability/interpolation.py
+++ b/ramannoodle/polarizability/interpolation.py
@@ -81,9 +81,9 @@ class InterpolationModel(PolarizabilityModel):
     Parameters
     ----------
     ref_structure
-        | Reference structure on which to base the model.
+        Reference structure on which to base the model.
     ref_polarizability
-        | 2D array with shape (3,3) with polarizability of the reference structure.
+        Array with shape (3,3) with polarizability of the reference structure.
     is_dummy_model
 
     """
@@ -117,7 +117,7 @@ def ref_polarizability(self) -> NDArray[np.float64]:
         Returns
         -------
         :
-            2D array with shape (3,3).
+            Array with shape (3,3).
         """
         return self._ref_polarizability.copy()
 
@@ -133,7 +133,7 @@ def cart_basis_vectors(self) -> list[NDArray[np.float64]]:
         Returns
         -------
         :
-            (Å) List of length J containing 2D arrays with shape (N,3) where J is the
+            (Å) List of length J containing arrays with shape (N,3) where J is the
             number of specified degrees of freedom and N is the number of atoms.
 
         """
@@ -157,7 +157,7 @@ def mask(self) -> NDArray[np.bool]:
         Returns
         -------
         :
-            1D array with shape (J,) where J is the number of specified degrees of
+            Array with shape (J,) where J is the number of specified degrees of
             freedom.
         """
         return self._mask.copy()
@@ -172,11 +172,9 @@ def mask(self, value: NDArray[np.bool]) -> None:
         Parameters
         ----------
         mask
-            1D array of size (N,) where N is the number of specified degrees
-            of freedom (DOFs).
-
-            If an element is False, its corresponding DOF will be "masked" and excluded
-            from polarizability calculations.
+            Array with shape (N,) where N is the number of specified degrees
+            of freedom (DOFs). If an element is False, its corresponding DOF will be
+            "masked" and excluded from polarizability calculations.
         """
         verify_ndarray_shape("mask", value, self._mask.shape)
         self._mask = value
@@ -189,17 +187,17 @@ def calc_polarizabilities(
         Parameters
         ----------
         positions_batch
-            | (fractional) 3D array with shape (S,N,3) where S is the number of samples
-            | and N is the number of atoms.
+            (fractional) Array with shape (S,N,3) where S is the number of samples
+            and N is the number of atoms.
 
         Returns
         -------
         :
-            3D array with shape (S,3,3).
+            Array with shape (S,3,3).
 
         Raises
         ------
-        UsageError
+        UserError
             Model is a dummy model.
 
         """
@@ -272,7 +270,11 @@ def _get_dof(  # pylint: disable=too-many-locals
         Returns
         -------
         :
-            3-tuple of the form (basis vectors, interpolation_xs, interpolation_ys)
+            0.  basis vectors -- (Å) List of length J containing arrays with shape (N,3)
+                where J is the number of degrees of freedom and N is the number of
+                atoms.
+            #.  interpolation_xs
+            #.  interpolation_ys
         """
         # Check that the parent displacement is orthogonal to existing basis vectors
         parent_cart_basis_vector = self._ref_structure.get_cart_displacement(
@@ -414,25 +416,23 @@ def add_dof(  # pylint: disable=too-many-arguments
         Parameters
         ----------
         cart_displacement
-            (Å) 2D array with shape (N,3) where N is the number of atoms.
-
-            Magnitude is arbitrary. Must be orthogonal to all previously added DOFs.
+            (Å) Array with shape (N,3) where N is the number of atoms. The magnitude of
+            the displacement is ignored, only the direction is used. Must be orthogonal
+            to all previously added DOFs.
         amplitudes
-            (Å) 1D array with shape (L,).
-
-            Duplicate amplitudes, either those explicitly provided or those generated
-            by structural symmetries, will raise :class:`.InvalidDOFException`.
+            (Å) Array with shape (L,). Duplicate amplitudes, either those explicitly
+            provided or those generated by structural symmetries, will raise
+            :class:`.InvalidDOFException`.
         polarizabilities
-            3D array with shape (1,3,3) or (2,3,3) containing known
-            polarizabilities for each amplitude.
-
-            If dummy model, this parameter is ignored.
+            Array with shape (1,3,3) or (2,3,3) containing known
+            polarizabilities for each amplitude. If dummy model, this parameter is
+            ignored.
         interpolation_order
-            | Must be less than the number of total number of amplitudes after
-            | symmetry considerations.
+            Must be less than the number of total number of amplitudes after
+            symmetry considerations.
         include_ref_polarizability
-            | Whether to include the references polarizability at 0.0 amplitude in the
-            | interpolation.
+            Whether to include the references polarizability at 0.0 amplitude in the
+            interpolation.
 
         Raises
         ------
@@ -485,9 +485,10 @@ def add_dof_from_files(
         ----------
         filepaths
         file_format
-            Supports ``"outcar"`` and ``"vasprun.xml"`` (see :ref:`Supported formats`).
+            Supports ``"outcar"`` and ``"vasprun.xml"``. If dummy model, supports
+            ``"poscar"`` and ``"xdatcar"`` as well (see :ref:`Supported formats`).
+
 
-            If dummy model, supports ``"poscar"`` and ``"xdatcar"`` as well.
 
         Raises
         ------
@@ -524,18 +525,21 @@ def _read_dof(
         Parameters
         ----------
         filepaths:
-            Supports: "outcar". If dummy model, supports: "outcar", "poscar" (see
-            :ref:`Supported formats`).
+            Supports ``"outcar"``. If dummy model, supports ``"outcar"``, | |
+            ``"poscar"`` (see :ref:`Supported formats`).
 
         Returns
         -------
         :
-            3-tuple with the form (displacements, polarizabilities, basis vector)
+            0.  displacements -- (fractional) Array with shape (J,N,3) where J is the
+                total number of displacements.
+            #.  amplitudes
+            #.  polarizabilities
 
         Raises
         ------
         FileNotFoundError
-            File could not be found.
+            File not found.
         InvalidDOFException
             DOF assembled from supplied files was invalid (see get_dof)
         """
diff --git a/ramannoodle/polarizability/torch/dataset.py b/ramannoodle/polarizability/torch/dataset.py
index e79736e..0d4a1f4 100644
--- a/ramannoodle/polarizability/torch/dataset.py
+++ b/ramannoodle/polarizability/torch/dataset.py
@@ -29,21 +29,17 @@ def _scale_and_flatten_polarizabilities(
     Parameters
     ----------
     polarizabilities
-        | 3D tensor with size [S,3,3] where S is the number of samples.
+        Tensor with size [S,3,3] where S is the number of samples.
     scale_mode
-        | Supports ``"standard"`` (standard scaling), ``"stddev"`` (division by
-        | standard deviation), and ``"none"`` (no scaling).
+        Supports ``"standard"`` (standard scaling), ``"stddev"`` (division by
+        standard deviation), and ``"none"`` (no scaling).
 
     Returns
     -------
     :
-        3-tuple:
-            0. | mean --
-               | Element-wise mean of polarizabilities.
-            #. | standard deviation --
-               | Element-wise standard deviation of polarizabilities.
-            #. | polarizability vectors --
-               | 2D tensor with size [S,6].
+        0.  mean -- Element-wise mean of polarizabilities.
+        #.  standard deviation -- Element-wise standard deviation of polarizabilities.
+        #.  polarizability vectors -- Tensor with size [S,6].
 
     """
     rn_torch_utils.verify_tensor_size(
@@ -79,16 +75,16 @@ class PolarizabilityDataset(Dataset[tuple[Tensor, Tensor, Tensor, Tensor]]):
     Parameters
     ----------
     lattice
-        | (Å) Array with shape (3,3).
+        (Å) Array with shape (3,3).
     atomic_numbers
-        | List of length N where N is the number of atoms.
+        List of length N where N is the number of atoms.
     positions
-        | (fractional) 3D array with shape (S,N,3) where S is the number of samples.
+        (fractional) Array with shape (S,N,3) where S is the number of samples.
     polarizabilities
-        | 3D array with shape (S,3,3).
+        Array with shape (S,3,3).
     scale_mode
-        | Supports ``"standard"`` (standard scaling), ``"stddev"`` (division by
-        | standard deviation), and ``"none"`` (no scaling).
+        Supports ``"standard"`` (standard scaling), ``"stddev"`` (division by
+        standard deviation), and ``"none"`` (no scaling).
 
     """
 
@@ -160,7 +156,7 @@ def polarizabilities(self) -> NDArray[np.float64]:
         Returns
         -------
         :
-            3D array with shape (S,3,3) where S is the number of samples.
+            Array with shape (S,3,3) where S is the number of samples.
         """
         return self._polarizabilities.detach().clone().numpy()
 
@@ -171,7 +167,7 @@ def scaled_polarizabilities(self) -> NDArray[np.float64]:
         Returns
         -------
         :
-            2D array with shape (S,6) where S is the number of samples.
+            Array with shape (S,6) where S is the number of samples.
         """
         return self._scaled_polarizabilities.detach().clone().numpy()
 
@@ -182,7 +178,7 @@ def mean_polarizability(self) -> NDArray[np.float64]:
         Return
         ------
         :
-            2D array with shape (3,3).
+            Array with shape (3,3).
         """
         return self._polarizabilities.mean(0).clone().numpy()
 
@@ -193,7 +189,7 @@ def stddev_polarizability(self) -> NDArray[np.float64]:
         Return
         ------
         :
-            2D array with shape (3,3).
+            Array with shape (3,3).
         """
         result = self._polarizabilities.std(0, unbiased=False)
         return result.clone().numpy()
@@ -209,9 +205,9 @@ def scale_polarizabilities(
         Parameters
         ----------
         mean
-            | Array with shape (3,3).
+            Array with shape (3,3).
         stddev
-            | Array with shape (3,3).
+            Array with shape (3,3).
 
         """
         verify_ndarray_shape("mean", mean, (3, 3))
diff --git a/ramannoodle/polarizability/torch/gnn.py b/ramannoodle/polarizability/torch/gnn.py
index 6bd589d..d517f86 100644
--- a/ramannoodle/polarizability/torch/gnn.py
+++ b/ramannoodle/polarizability/torch/gnn.py
@@ -50,7 +50,7 @@ class _GaussianFilter(torch.nn.Module):
     lower_bound
     upper_bound
     steps
-        | Number of steps to take between lower_bound and upper_bound.
+        Number of steps to take between ``lower_bound`` and ``upper_bound``.
     """
 
     def __init__(self, lower_bound: float, upper_bound: float, steps: int):
@@ -65,12 +65,12 @@ def forward(self, x: Tensor) -> Tensor:
         Parameters
         ----------
         x
-            | 1D tensor with size [D,]. Typically contains interatomic distances.
+            Tensor with size [D,]. Typically contains interatomic distances.
 
         Returns
         -------
         :
-            2D tensor with size [D,steps].
+            Tensor with size [D,steps].
 
         """
         x = x.view(-1, 1) - self.offset.view(1, -1)
@@ -122,16 +122,16 @@ def forward(
         Parameters
         ----------
         node_embedding
-            | 2D tensor with size [N,size_node_embedding] where N is the number of
-            | nodes.
+            Tensor with size [N,size_node_embedding] where N is the number of
+            nodes.
         edge_embedding
-            | 2D tensor with size [E,size_edge_embedding] where E is the number of
-            | edges.
+            Tensor with size [E,size_edge_embedding] where E is the number of
+            edges.
 
         Returns
         -------
         :
-            2D tensor with size [N,size_node_embedding].
+            Tensor with size [N,size_node_embedding].
         """
         c1 = torch.cat([node_embedding[i], edge_embedding], dim=1)
         c1 = self.c1_norm(self.c1_linear(c1))
@@ -203,17 +203,17 @@ def _get_c2_embedding(
         Parameters
         ----------
         node_embedding
-            | 2D tensor with size [N,size_node_embedding] where N is the number of
-            | nodes.
+            Tensor with size [N,size_node_embedding] where N is the number of
+            nodes.
         i
-            | Node 1 of edge pairs, a 1D tensor with size [E,].
+            Node 1 of edge pairs, a tensor with size [E,].
         j
-            | Node 2 of edge pairs, a 1D tensor with size [E,].
+            Node 2 of edge pairs, a tensor with size [E,].
 
         Returns
         -------
         :
-            2D tensor with size [E,size_edge_embedding].
+            Tensor with size [E,size_edge_embedding].
         """
         c2 = node_embedding[i] * node_embedding[j]
         c2 = self.c2_norm_1(self.c2_linear(c2))
@@ -237,29 +237,29 @@ def _get_c3_embedding(  # pylint: disable=too-many-arguments
         Parameters
         ----------
         node_embedding
-            | 2D tensor with size [N,size_node_embedding] where N is the number of
-            | nodes.
+            Tensor with size [N,size_node_embedding] where N is the number of
+            nodes.
         edge_embedding
-            | 2D tensor with size [E,size_edge_embedding] where E is the number of
-            | edges.
+            Tensor with size [E,size_edge_embedding] where E is the number of
+            edges.
         index_i
-            | Node 1 of edge triplets, a 1D tensor with size [T,] where T is the number
-            | of triplets.
+            Node 1 of edge triplets, a tensor with size [T,] where T is the number
+            of triplets.
         index_j
-            | Node 2 of edge triplets, a 1D tensor with size [T,].
+            Node 2 of edge triplets, a tensor with size [T,].
         index_k
-            | Node 3 of edge triplets, a 1D tensor with size [T,].
+            Node 3 of edge triplets, a tensor with size [T,].
         index_ji
-            | Index of (j,i) corresponding to (index_j,index_i), a 1D tensor with size
-            | [T,.]
+            Index of (j,i) corresponding to (index_j,index_i), a tensor with size
+            [T,].
         index_kj
-            | Index of (k,j) corresponding to (index_k,index_j), a 1D tensor with size
-            | [T,.]
+            Index of (k,j) corresponding to (index_k,index_j), a tensor with size
+            [T,].
 
         Returns
         -------
         :
-            2D tensor with size [E,size_edge_embedding].
+            Tensor with size [E,size_edge_embedding].
         """
         c3 = torch.cat(
             [
@@ -301,33 +301,33 @@ def forward(  # pylint: disable=too-many-arguments
         Parameters
         ----------
         node_embedding
-            | 2D tensor with size [N,size_node_embedding] where N is the number of
-            | nodes.
+            Tensor with size [N,size_node_embedding] where N is the number of
+            nodes.
         edge_embedding
-            | 2D tensor with size [E,size_edge_embedding] where E is the number of
-            | edges.
+            Tensor with size [E,size_edge_embedding] where E is the number of
+            edges.
         i
-            | Node 1 of edge pairs, a 1D tensor with size [E,].
+            Node 1 of edge pairs, a tensor with size [E,].
         j
-            | Node 2 of edge pairs, a 1D tensor with size [E,].
+            Node 2 of edge pairs, a tensor with size [E,].
         index_i
-            | Node 1 of edge triplets, a 1D tensor with size [T,] where T is the number
-            | of triplets.
+            Node 1 of edge triplets, a tensor with size [T,] where T is the number
+            of triplets.
         index_j
-            | Node 2 of edge triplets, a 1D tensor with size [T,].
+            Node 2 of edge triplets, a tensor with size [T,].
         index_k
-            | Node 3 of edge triplets, a 1D tensor with size [T,].
+            Node 3 of edge triplets, a tensor with size [T,].
         index_ji
-            | Index of (j,i) corresponding to (index_j,index_i), a 1D tensor with size
-            | [T,.]
+            Index of (j,i) corresponding to (index_j,index_i), a tensor with size
+            [T,].
         index_kj
-            | Index of (k,j) corresponding to (index_k,index_j), a 1D tensor with size
-            | [T,.]
+            Index of (k,j) corresponding to (index_k,index_j), a tensor with size
+            [T,].
 
         Returns
         -------
         :
-            2D tensor with size [E,size_edge_embedding].
+            Tensor with size [E,size_edge_embedding].
 
         """
         c2_embedding = self._get_c2_embedding(node_embedding, i, j)
@@ -353,14 +353,14 @@ def _get_edge_polarizability_vectors(
     Parameters
     ----------
     polarizability_embedding
-        | 2D tensor with size [E,12] where E is the number of edges.
+        Tensor with size [E,12] where E is the number of edges.
     unit_vector
-        | (Å) Unit vectors of edges, a 2D tensor with size [E,3].
+        (Å) Unit vectors of edges, a tensor with size [E,3].
 
     Returns
     -------
     :
-        2D tensor with size [E,6].
+        Tensor with size [E,6].
     """
     a1 = torch.zeros((polarizability_embedding.size(0), 3, 3))
     a1[:, 0, 0] = polarizability_embedding[:, 0]
@@ -413,24 +413,33 @@ class PotGNN(
 ):  # pylint: disable=too-many-instance-attributes
     r"""POlarizability Tensor Graph Neural Network (PotGNN).
 
-    GNN architecture was inspired by the "direct force architecture" developed in Park
-    `et al.`;  `npj Computational Materials` (2021)7:73; https://doi.org/10.1038/
-    s41524-021-00543-3. Implementation adapted from ``torch_geometric.nn.models.GNNFF``
+    The architecture was inspired by the "direct force architecture" developed in Park
+    `et al.`;  `npj Computational Materials` (2021)7:73;
+    `doi:10.1038/s41524-021-00543-3 <https://doi.org/10.1038/s41524-021-00543-3>`_.
+    Implementation adapted from ``torch_geometric.nn.models.GNNFF``
     authored by @ken2403 and merged by @rusty1s.
 
+    The architecture of this model is still somewhat in flux. More complete
+    documentation for this model, including a description of the architecture and
+    discussion of design choices, will be available at a later date.
+
     Parameters
     ----------
     ref_structure
-        | Reference structure from which nodes/edges are determined.
+        Reference structure from which nodes/edges are determined.
     cutoff
-        | (Å) Cutoff distance for edges.
+        (Å) Cutoff distance for edges.
     size_node_embedding
     size_edge_embedding
     num_message_passes
     gaussian_filter_start
-        | (Å) Lower bound of the Gaussian filter used in initial edge embedding.
+        (Å) Lower bound of the Gaussian filter used in initial edge embedding.
     gaussian_filter_end
-        | (Å) Upper bound of the Gaussian filter used in initial edge embedding.
+        (Å) Upper bound of the Gaussian filter used in initial edge embedding.
+    mean_polarizability
+        Array with shape (3,3).
+    stddev_polarizability
+        Array with shape (3,3).
     """
 
     def __init__(  # pylint: disable=too-many-arguments,too-many-locals
@@ -529,7 +538,7 @@ def _convert_to_atom_type(self, atomic_numbers: Tensor) -> Tensor:
         Parameters
         ----------
         atomic_numbers
-            | Tensor with arbitrary shape.
+            Tensor with arbitrary shape.
 
         Returns
         -------
@@ -557,22 +566,20 @@ def _batch_graph(
         Parameters
         ----------
         lattice
-            | (Å) Tensor with size [S,3,3] where S is the number of samples.
+            (Å) Tensor with size [S,3,3] where S is the number of samples.
         positions
-            | (fractional) Tensor with size [S,N,3] where N is the number of atoms.
+            (fractional) Tensor with size [S,N,3] where N is the number of atoms.
 
         Returns
         -------
         :
-            3-tuple:
-            0. | edge indexes --
-               | 2D Tensor of size [3,E] where E is the number of edges. The first
-               | element are the graph indexes, while the remaining two elements are
-               | edge indexes.
-            #. | unit vectors --
-               | (Å) 2D Tensor with size [E,3].
-            #. | distances --
-               | (Å) 1D Tensor with size [E,].
+            0.  edge indexes -- Tensor of size [3,E] where E is the number of edges. The
+                first element are the graph indexes, while the remaining two elements
+                are edge indexes.
+
+            #.  unit vectors -- (Å) Tensor with size [E,3].
+
+            #.  distances -- (Å) Tensor with size [E,].
 
         """
         num_samples = lattice.size(0)
@@ -611,11 +618,11 @@ def forward(  # pylint: disable=too-many-locals
         Parameters
         ----------
         lattice
-            | (Å) 3D tensor with size [S,3,3] where S is the number of samples.
+            (Å) Tensor with size [S,3,3] where S is the number of samples.
         atomic_numbers
-            | Tensor with size [S,N] where N is the number of atoms.
+            Tensor with size [S,N] where N is the number of atoms.
         positions
-            | (fractional) Tensor with size [S,N,3].
+            (fractional) Tensor with size [S,N,3].
 
         Returns
         -------
@@ -658,13 +665,13 @@ def calc_polarizabilities(
         Parameters
         ----------
         positions_batch
-            | (fractional) 3D array with shape (S,N,3) where S is the number of samples
-            | and N is the number of atoms.
+            (fractional) Array with shape (S,N,3) where S is the number of samples
+            and N is the number of atoms.
 
         Returns
         -------
         :
-            3D array with shape (S,3,3).
+            Array with shape (S,3,3).
         """
         verify_ndarray_shape(
             "positions_batch", positions_batch, (None, self._ref_structure.num_atoms, 3)
diff --git a/ramannoodle/polarizability/torch/train.py b/ramannoodle/polarizability/torch/train.py
index bf31a31..4b18d68 100644
--- a/ramannoodle/polarizability/torch/train.py
+++ b/ramannoodle/polarizability/torch/train.py
@@ -42,10 +42,9 @@ def train_single_epoch(  # pylint: disable=too-many-arguments,too-many-locals
     Returns
     -------
     :
-        0.  | mean training loss --
-        #.  | mean validation loss --
-        #.  | mean variance of predictions on validation set --
-            | Array with shape [6,]
+        0.  mean training loss
+        #.  mean validation loss
+        #.  mean variance of predictions on validation set -- Array with shape [6,]
 
     """
     default_device = torch.get_default_device()
diff --git a/ramannoodle/polarizability/torch/utils.py b/ramannoodle/polarizability/torch/utils.py
index fc67a7a..d16a465 100644
--- a/ramannoodle/polarizability/torch/utils.py
+++ b/ramannoodle/polarizability/torch/utils.py
@@ -28,12 +28,12 @@ def polarizability_vectors_to_tensors(polarizability_vectors: Tensor) -> Tensor:
     Parameters
     ----------
     polarizability_vectors
-        | 2D Tensor with size [S,6].
+        Tensor with size [S,6].
 
     Returns
     -------
     :
-        3D tensor with size [S,3,3].
+        Tensor with size [S,3,3].
     """
     verify_tensor_size("polarizability_vectors", polarizability_vectors, (None, 6))
     indices = torch.tensor(
@@ -52,12 +52,12 @@ def polarizability_tensors_to_vectors(polarizability_tensors: Tensor) -> Tensor:
     Parameters
     ----------
     polarizability_tensors
-        | 3D tensor with size [S,3,3] where S is the number of samples.
+        Tensor with size [S,3,3] where S is the number of samples.
 
     Returns
     -------
     :
-        2D tensor with size [S,6].
+        Tensor with size [S,6].
 
     """
     verify_tensor_size("polarizability_tensors", polarizability_tensors, (None, 3, 3))
@@ -73,7 +73,7 @@ def _get_tensor_size_str(size: Sequence[int | None]) -> str:
     Parameters
     ----------
     size
-        | None indicates dimension can be any size.
+        None indicates dimension can be any size.
     """
     result = "["
     for i in size:
@@ -121,12 +121,12 @@ def get_rotations(targets: Tensor) -> Tensor:
     Parameters
     ----------
     targets
-        | 2D tensor with size [S,3]. Vectors do not need to be normalized.
+        Tensor with size [S,3]. Vectors do not need to be normalized.
 
     Returns
     -------
     :
-        3D tensor with size [S,3,3].
+        Tensor with size [S,3,3].
     """
     reference = torch.zeros(targets.size())
     reference[:, 0] = 1
@@ -165,7 +165,10 @@ def get_graph_info(
     cart_distance_matrix: Tensor,
     num_atoms: int,
 ) -> tuple[Tensor, Tensor, Tensor]:
-    """Get information on graph."""
+    """Get information on graph.
+
+    :meta: private
+    """
     cart_unit_vectors = cart_displacement[
         edge_indexes[0], edge_indexes[1], edge_indexes[2]
     ]  # python 3.10 complains if we use the unpacking operator (*)
@@ -189,22 +192,21 @@ def _radius_graph_pbc(
     Parameters
     ----------
     lattice
-        | (Å) 3D tensor with size [S,3,3] where S is the number of samples.
+        (Å) Tensor with size [S,3,3] where S is the number of samples.
     positions
-        | (fractional) 3D tensor with size [S,N,3] where N is the number of atoms.
+        (fractional) Tensor with size [S,N,3] where N is the number of atoms.
     cutoff
-        | Edge cutoff distance.
+        Edge cutoff distance.
 
     Returns
     -------
     :
-        3-tuple.
-        First element is edge indexes, a tensor of size [3,X] where X is the number of
-        edges. This tensor defines S non-interconnected graphs making up a batch. The
-        first row defines the graph index. The second and third rows define the actual
-        edge indexes used by ``triplet``.
-        Second element is cartesian unit vectors, a tensor of size [X,3].
-        Third element is distances, a tensor of side [X,1].
+        0.  edge indexes -- Tensor of size [3,X] where X is the number of edges. This
+            tensor defines S non-interconnected graphs making up a batch. The first row
+            defines the graph index. The second and third rows define the actual edge
+            indexes used by ``triplet``.
+        1.  cartesian unit vectors -- (Å) Tensor with size [X,3].
+        2.  distances -- (Å) Tensor with size [X,1].
 
     """
     num_samples = lattice.size(0)
@@ -272,24 +274,16 @@ def get_triplets(
         Returns
         -------
         :
-            7-tuple:
-                0. | i --
-                   | Node 1 of edge pairs, a 1D tensor with size [E,].
-                #. | j --
-                   | Node 2 of edge pairs, a 1D tensor with size [E,].
-                #. | index_i --
-                   | Node 1 of edge triplets, a 1D tensor with size [T,] where T is the
-                   | number of triplets.
-                #. | index_j --
-                   | Node 2 of edge triplets, a 1D tensor with size [T,].
-                #. | index_k --
-                   | Node 3 of edge triplets, a 1D tensor with size [T,].
-                #. | index_ji --
-                   | Index of (j,i) corresponding to (index_j,index_i), a 1D tensor
-                   | with size [T,].
-                #. | index_kj --
-                   | Index of (k,j) corresponding to (index_k,index_j), a 1D tensor
-                   | with size [T,].
+            0.  i -- Node 1 of edge pairs, a tensor with size [E,].
+            #.  j -- Node 2 of edge pairs, a tensor with size [E,].
+            #.  index_i -- Node 1 of edge triplets, a tensor with size [T,] where T is
+                the number of triplets.
+            #.  index_j -- Node 2 of edge triplets, a tensor with size [T,].
+            #.  index_k -- Node 3 of edge triplets, a tensor with size [T,].
+            #.  index_ji -- Index of (j,i) corresponding to (index_j,index_i), a tensor
+                with size [T,].
+            #.  index_kj -- Index of (k,j) corresponding to (index_k,index_j), a tensor
+                with size [T,].
 
         """
         if batch_size != self._cached_batch_size:
@@ -333,15 +327,15 @@ def batch_positions(
     Parameters
     ----------
     positions
-        | (fractional) 3D array with shape (S,N,3) where S is the number of samples
-        | and N is the number of atoms.
+        (fractional) Array with shape (S,N,3) where S is the number of samples
+        and N is the number of atoms.
     batch_size
-        | Split positions into batches of size ``batch_size``.
+        Split positions into batches of size ``batch_size``.
 
     Yields
     ------
     :
-        3D array with shape (batch_size,N,3).
+        Array with shape (batch_size,N,3).
 
     """
     verify_ndarray_shape("positions", positions, (None, None, 3))
diff --git a/ramannoodle/spectrum/abstract.py b/ramannoodle/spectrum/abstract.py
index e3e14ca..e863546 100644
--- a/ramannoodle/spectrum/abstract.py
+++ b/ramannoodle/spectrum/abstract.py
@@ -23,25 +23,22 @@ def measure(  # pylint: disable=too-many-arguments
         Parameters
         ----------
         orientation
-            Supports ``"polycrystalline"``.
-
-            Future versions will support arbitrary orientations.
+            Supports ``"polycrystalline"``. Future versions will support arbitrary
+            orientations.
         laser_correction
-            | Whether to apply laser-wavelength-dependent intensity correction.
+            If ``True``, applies laser-wavelength-dependent intensity correction.
         laser_wavelength
-            | (nm) Ignored if ``laser_correction == False``.
+            (nm) Ignored if ``laser_correction == False``.
         bose_einstein_correction
-            | Whether to apply temperature-dependent Bose Einstein correction.
+            If ``True``, applies temperature-dependent Bose Einstein correction.
         temperature
-            | (K) Ignored if ``bose_einstein_correction == False``.
+            (K) Ignored if ``bose_einstein_correction == False``.
 
         Returns
         -------
         :
-            2-tuple:
-                0. | wavenumbers --
-                   | (cm\ :sup:`-1`) 1D array with shape (M,).
-                #. | intensities --
-                   | (arbitrary units) 1D array with shape (M,).
+            0. wavenumbers -- (cm\ :sup:`-1`) Array with shape (M,).
+
+            #. intensities -- (arbitrary units) Array with shape (M,).
 
         """
diff --git a/ramannoodle/spectrum/raman.py b/ramannoodle/spectrum/raman.py
index ed7a32a..6f39ee1 100644
--- a/ramannoodle/spectrum/raman.py
+++ b/ramannoodle/spectrum/raman.py
@@ -18,14 +18,14 @@ def get_bose_einstein_correction(
     Parameters
     ----------
     wavenumbers
-        | (cm\ :sup:`-1`) 1D array with shape (M,).
+        (cm\ :sup:`-1`) Array with shape (M,).
     temperature
-        | (K)
+        (K)
 
     Returns
     -------
     :
-        1D array with shape (M,).
+        Array with shape (M,).
 
     """
     try:
@@ -48,14 +48,14 @@ def get_laser_correction(
     Parameters
     ----------
     wavenumbers
-        | (cm\ :sup:`-1`) 1D array with shape (M,).
+        (cm\ :sup:`-1`) Array with shape (M,).
     laser_wavenumber
-        | (cm\ :sup:`-1`)
+        (cm\ :sup:`-1`)
 
     Returns
     -------
     :
-        1D array with shape (M,).
+        Array with shape (M,).
 
     """
     try:
@@ -78,9 +78,9 @@ class PhononRamanSpectrum(RamanSpectrum):
     Parameters
     ----------
     phonon_wavenumbers
-        | (cm\ :sup:`-1`) 1D array with shape (M,) where M is the number of phonons.
+        (cm\ :sup:`-1`) Array with shape (M,) where M is the number of phonons.
     raman_tensors
-        | 3D array with shape (M,3,3).
+        Array with shape (M,3,3).
 
     """
 
@@ -103,7 +103,7 @@ def phonon_wavenumbers(self) -> NDArray[np.float64]:
         Returns
         -------
         :
-            (cm\ :sup:`-1`) 1D array with shape (M,) where M is the number of phonons.
+            (cm\ :sup:`-1`) Array with shape (M,) where M is the number of phonons.
         """
         return self._phonon_wavenumbers.copy()
 
@@ -114,7 +114,7 @@ def raman_tensors(self) -> NDArray[np.float64]:
         Returns
         -------
         :
-            3D array with shape (M,3,3) where M is the number of phonons.
+            Array with shape (M,3,3) where M is the number of phonons.
         """
         return self._raman_tensors.copy()
 
@@ -131,26 +131,22 @@ def measure(  # pylint: disable=too-many-arguments
         Parameters
         ----------
         orientation
-            Supports ``"polycrystalline"``.
-
-            Future versions will support arbitrary orientations.
+            Supports ``"polycrystalline"``. Future versions will support arbitrary
+            orientations.
         laser_correction
-            | Whether to apply laser-wavelength-dependent intensity correction.
+            If ``True``, applies laser-wavelength-dependent intensity correction.
         laser_wavelength
-            | (nm) Ignored if ``laser_correction == False``.
+            (nm) Ignored if ``laser_correction == False``.
         bose_einstein_correction
-            | Whether to apply temperature-dependent Bose Einstein correction.
+            If ``True``, applies temperature-dependent Bose Einstein correction.
         temperature
-            | (K) Ignored if ``bose_einstein_correction == False``.
+            (K) Ignored if ``bose_einstein_correction == False``.
 
         Returns
         -------
         :
-            2-tuple:
-                0. | wavenumbers --
-                   | (cm\ :sup:`-1`) 1D array with shape (M,).
-                #. | intensities --
-                   | (arbitrary units) 1D array with shape (M,).
+            0.  wavenumbers -- (cm\ :sup:`-1`) Array with shape (M,).
+            #.  intensities -- (arbitrary units) Array with shape (M,).
 
         Raises
         ------
@@ -206,9 +202,9 @@ class MDRamanSpectrum(RamanSpectrum):
     Parameters
     ----------
     polarizability_ts
-        | 3D array with shape (S,3,3) where S is the number of configurations.
+        Array with shape (S,3,3) where S is the number of configurations.
     timestep
-        | (fs)
+        (fs)
 
     """
 
@@ -225,7 +221,7 @@ def polarizability_ts(self) -> NDArray[np.float64]:
         Returns
         -------
         :
-            3D array with shape (S,3,3) where S is the number of configurations.
+            Array with shape (S,3,3) where S is the number of configurations.
         """
         return self._polarizability_ts
 
@@ -256,27 +252,23 @@ def measure(  # pylint: disable=too-many-arguments
         Parameters
         ----------
         orientation
-            Supports ``"polycrystalline"``.
-
-            Future versions will support arbitrary orientations.
+            Supports ``"polycrystalline"``. Future versions will support arbitrary
+            orientations.
         laser_correction
-            | Whether to apply laser-wavelength-dependent intensity correction.
+            If ``True``, applies laser-wavelength-dependent intensity correction.
         laser_wavelength
-            | (nm) Ignored if ``laser_correction == False``.
+            (nm) Ignored if ``laser_correction == False``.
         bose_einstein_correction
-            | Whether to apply temperature-dependent Bose Einstein correction.
+            If ``True``, applies temperature-dependent Bose Einstein correction.
         temperature
-            | (K) Ignored if ``bose_einstein_correction == False``.
+            (K) Ignored if ``bose_einstein_correction == False``.
 
         Returns
         -------
         :
-            2-tuple:
-                0. | wavenumbers --
-                   | (cm\ :sup:`-1`) 1D array with shape (ceiling(S / 2),) where S is
-                   | the number of configurations.
-                #. | intensities --
-                   | (arbitrary units) 1D array with shape (ceiling(S / 2),).
+            0.  wavenumbers -- (cm\ :sup:`-1`) Array with shape (ceiling(S / 2),) where
+                S is the number of configurations.
+            #.  intensities -- (arbitrary units) Array with shape (ceiling(S / 2),).
 
         """
         if orientation != "polycrystalline":
diff --git a/ramannoodle/spectrum/spectrum_utils.py b/ramannoodle/spectrum/spectrum_utils.py
index 2877081..a5a796c 100644
--- a/ramannoodle/spectrum/spectrum_utils.py
+++ b/ramannoodle/spectrum/spectrum_utils.py
@@ -21,26 +21,22 @@ def convolve_spectrum(
     Parameters
     ----------
     wavenumbers
-        | (cm\ :sup:`-1`) 1D array with shape (M,).
+        (cm\ :sup:`-1`) Array with shape (M,).
     intensities
-        | (arbitrary units) 1D array with shape (M,).
+        (arbitrary units) Array with shape (M,).
     function
-        | Supports ``"gaussian"`` or ``"lorentzian"``.
+        Supports ``"gaussian"`` or ``"lorentzian"``.
     width
-        | (cm\ :sup:`-1`)
+        (cm\ :sup:`-1`)
     out_wavenumbers
-        (cm\ :sup:`-1`) 1D array with shape (L,) where L is arbitrary.
-
-        If None, ``out_wavenumbers`` is determined automatically.
+        (cm\ :sup:`-1`) Array with shape (L,) where L is arbitrary. If ``None``,
+        ``out_wavenumbers`` is determined automatically.
 
     Returns
     -------
     :
-        2-tuple:
-                0. | wavenumbers (``out_wavenumbers``) --
-                   | (cm\ :sup:`-1`) 1D array with shape (L,).
-                #. | intensities --
-                   | (arbitrary units) 1D array with shape (L,).
+        0.  wavenumbers (``out_wavenumbers``) -- (cm\ :sup:`-1`) Array with shape (L,).
+        #.  intensities -- (arbitrary units) Array with shape (L,).
 
     """
     if out_wavenumbers is None:
@@ -98,7 +94,7 @@ def _calc_autocorrelation(signal: NDArray[np.float64]) -> NDArray[np.float64]:
 
 def calc_signal_spectrum(
     signal: NDArray[np.float64], sampling_rate: float
-) -> NDArray[np.float64]:
+) -> tuple[NDArray[np.float64], NDArray[np.float64]]:
     r"""Calculate a signal's spectrum.
 
     The spectrum is  defined as the positive-frequency Fourier transform of the
@@ -107,18 +103,15 @@ def calc_signal_spectrum(
     Parameters
     ----------
     signal
-        | Array with shape (S,) where S is the number of samples.
+        Array with shape (S,) where S is the number of samples.
     sampling_rate
-        | (fs)
+        (fs)
 
     Returns
     -------
     :
-        2-tuple:
-            0. | wavenumbers --
-               | (cm\ :sup:`-1`) 1D array with shape (ceiling(S / 2),).
-            #. | intensities --
-               | (arbitrary units) 1D array with shape (ceiling(S / 2),).
+        0.  wavenumbers -- (cm\ :sup:`-1`) Array with shape (ceiling(S / 2),).
+        #.  intensities -- (arbitrary units) Array with shape (ceiling(S / 2),).
 
     """
     autocorrelation = _calc_autocorrelation(signal)
diff --git a/ramannoodle/structure/displace.py b/ramannoodle/structure/displace.py
index 2b60832..1beeaa2 100644
--- a/ramannoodle/structure/displace.py
+++ b/ramannoodle/structure/displace.py
@@ -38,18 +38,17 @@ def get_displaced_positions(
     Parameters
     ----------
     ref_structure
-        | Reference structure containing N atoms.
+        Reference structure containing N atoms.
     cart_displacement
-        (Å) 2D array with shape (N,3).
-
-        Magnitude is arbitrary.
+        (Å) Array with shape (N,3). The magnitude of the displacement is ignored,
+        only the direction is used.
     amplitudes
-        | (Å) 1D array with shape (M,).
+        (Å) Array with shape (M,).
 
     Returns
     -------
     :
-        (fractional) List of length M containing 2D arrays with shape (N,3).
+        (fractional) List of length M containing arrays with shape (N,3).
 
     """
     try:
@@ -88,18 +87,17 @@ def write_displaced_structures(  # pylint: disable=too-many-arguments
     Parameters
     ----------
     ref_structure
-        | Reference structure containing N atoms
+        Reference structure containing N atoms
     cart_displacement
-        (Å) 2D array with shape (N,3).
-
-        Magnitude is arbitrary.
+        (Å) Array with shape (N,3). The magnitude of the displacement is ignored,
+        only the direction is used.
     amplitudes
-        | (Å) 1D array with shape (M,).
+        (Å) Array with shape (M,).
     filepaths
     file_format
-        | Supports ``"poscar"`` (see :ref:`Supported formats`).
+        Supports ``"poscar"`` (see :ref:`Supported formats`).
     overwrite
-        | Overwrite the file if it exists.
+        If ``True``, overwrite the file if it exists.
     """
     filepaths = pathify_as_list(filepaths)
     position_list = get_displaced_positions(
@@ -129,19 +127,18 @@ def get_ast_displaced_positions(
     Parameters
     ----------
     ref_structure
-        | Reference structure containing N atoms.
+        Reference structure containing N atoms.
     atom_index
     cart_direction
-        (Å) 1D array with shape (3,).
-
-        Magnitude is arbitrary.
+        (Å) Array with shape (3,).  The magnitude of the direction vector is ignored,
+        only the direction is used.
     amplitudes
-        | (Å) 1D array with shape (M,).
+        (Å) Array with shape (M,).
 
     Returns
     -------
     :
-        (fractional) List of length M containing 2D arrays with shape (N,3).
+        (fractional) List of length M containing arrays with shape (N,3).
     """
     try:
         cart_direction = cart_direction / float(np.linalg.norm(cart_direction))
@@ -175,16 +172,15 @@ def write_ast_displaced_structures(  # pylint: disable=too-many-arguments
         Reference structure containing N atoms.
     atom_index
     cart_direction
-        | (Å) 1D array with shape (3,).
-
-        Magnitude is arbitrary.
+        (Å) Array with shape (3,). The magnitude of the direction vector is ignored,
+        only the direction is used.
     amplitudes
-        | (Å) 1D array with shape (M,).
+        (Å) Array with shape (M,).
     filepaths
     file_format
-        | Supports ``"poscar"`` (see :ref:`Supported formats`).
+        Supports ``"poscar"`` (see :ref:`Supported formats`).
     overwrite
-        | Overwrite the file if it exists.
+        Overwrite the file if it exists.
     """
     filepaths = pathify_as_list(filepaths)
     position_list = get_ast_displaced_positions(
diff --git a/ramannoodle/structure/reference.py b/ramannoodle/structure/reference.py
index 290b85b..ccea379 100644
--- a/ramannoodle/structure/reference.py
+++ b/ramannoodle/structure/reference.py
@@ -54,9 +54,9 @@ def _get_positions_permutation_matrix(
     Parameters
     ----------
     reference_positions
-        A 2D array with shape (N,3)
+        (fractional) Array with shape (N,3)
     permuted_positions
-        A 2D array with shape (N,3).
+        (fractional) Array with shape (N,3).
 
     """
     # Compute pairwise distance matrix.
@@ -78,15 +78,15 @@ class ReferenceStructure:
     Parameters
     ----------
     atomic_numbers
-        | List of length N where N is the number of atoms.
+        List of length N where N is the number of atoms.
     lattice
-        | (Å) 2D array with shape (3,3).
+        (Å) Array with shape (3,3).
     positions
-        | (fractional) 2D array with shape (N,3).
+        (fractional) Array with shape (N,3).
     symprec
-        | (Å) Distance tolerance for symmetry search (spglib).
+        (Å) Distance tolerance for symmetry search (spglib).
     angle_tolerance
-        | (°) Angle tolerance for symmetry search (spglib).
+        (°) Angle tolerance for symmetry search (spglib).
 
     Raises
     ------
@@ -141,7 +141,7 @@ def lattice(self) -> NDArray[np.float64]:
         Returns
         -------
         :
-            Å | 2D array with shape (3,3).
+            Å | Array with shape (3,3).
         """
         return self._lattice.copy()
 
@@ -152,7 +152,7 @@ def positions(self) -> NDArray[np.float64]:
         Returns
         -------
         :
-            (fractional) 2D array with shape (N,3) where N is the number of atoms.
+            (fractional) Array with shape (N,3) where N is the number of atoms.
         """
         return self._positions.copy()
 
@@ -168,8 +168,7 @@ def get_equivalent_atom_dict(self) -> dict[int, list[int]]:
         Returns
         -------
         :
-            dict:
-                | atom index --> list of equivalent atom indexes
+            atom index --> list of equivalent atom indexes
 
         """
         assert self._symmetry_dict is not None
@@ -189,7 +188,7 @@ def get_equivalent_displacements(
         Parameters
         ----------
         displacement
-            | (fractional) 2D array with shape (N,3) where N is the number of atoms.
+            (fractional) Array with shape (N,3) where N is the number of atoms.
 
         Returns
         -------
@@ -274,12 +273,12 @@ def get_cart_displacement(
         Parameters
         ----------
         displacement
-            | (fractional) Array with shape (...,N,3)  where N is the number of atoms.
+            (fractional) Array with shape (...,N,3)  where N is the number of atoms.
 
         Returns
         -------
         :
-            (Å) | Array with shape (...,N,3).
+            (Å) Array with shape (...,N,3).
         """
         displacement = apply_pbc_displacement(displacement)
 
@@ -291,12 +290,12 @@ def get_cart_direction(self, direction: NDArray[np.float64]) -> NDArray[np.float
         Parameters
         ----------
         direction
-            | (fractional) 1D array with shape (3,).
+            (fractional) Array with shape (3,).
 
         Returns
         -------
         :
-            (Å) 1D array with shape (3,).
+            (Å) Array with shape (3,).
         """
         direction = apply_pbc_displacement(direction)
         try:
@@ -312,12 +311,12 @@ def get_frac_displacement(
         Parameters
         ----------
         cart_displacement
-            | (Å) 2D array with shape (N,3) where N is the number of atoms.
+            (Å) Array with shape (N,3) where N is the number of atoms.
 
         Returns
         -------
         :
-            (fractional) 2D array with shape (N,3).
+            (fractional) Array with shape (N,3).
         """
         verify_ndarray_shape("cart_displacement", cart_displacement, (None, 3))
         displacement = (cart_displacement) @ np.linalg.inv(self.lattice)
@@ -331,12 +330,12 @@ def get_frac_direction(
         Parameters
         ----------
         cart_direction
-            | (Å) 1D array with shape (3,).
+            (Å) Array with shape (3,).
 
         Returns
         -------
         :
-            | (fractional) 1D array with shape (3,).
+            (fractional) Array with shape (3,).
         """
         verify_ndarray_shape("direction", cart_direction, (3,))
         displacement = np.array([cart_direction]) @ np.linalg.inv(self.lattice)
@@ -349,9 +348,8 @@ def get_atom_indexes(self, atom_symbols: str | list[str]) -> list[int]:
         ----------
         atom_symbols
             If integer or list of integers, specifies atom indexes. If string or list
-            of strings, specifies atom symbols.
-
-            Mixtures of indexes and symbols are allowed.
+            of strings, specifies atom symbols. Mixtures of indexes and symbols are
+            allowed.
         """
         symbols = [ATOM_SYMBOLS[number] for number in self._atomic_numbers]
         indexes = []
diff --git a/ramannoodle/structure/structure_utils.py b/ramannoodle/structure/structure_utils.py
index 18f406f..daf10cc 100644
--- a/ramannoodle/structure/structure_utils.py
+++ b/ramannoodle/structure/structure_utils.py
@@ -16,12 +16,12 @@ def apply_pbc(positions: NDArray[np.float64]) -> NDArray[np.float64]:
     Parameters
     ----------
     positions
-        | (fractional) 2D array with shape (N,3) where N is the number of atoms.
+        (fractional) Array with shape (N,3) where N is the number of atoms.
 
     Returns
     -------
     :
-        (fractional) 2D array with shape (N,3).
+        (fractional) Array with shape (N,3).
     """
     try:
         return positions - positions // 1
@@ -35,12 +35,12 @@ def apply_pbc_displacement(displacement: NDArray[np.float64]) -> NDArray[np.floa
     Parameters
     ----------
     displacement
-        | (fractional) 2D array with shape (N,3) where N is the number of atoms.
+        (fractional) Array with shape (N,3) where N is the number of atoms.
 
     Returns
     -------
     :
-        (fractional) 2D array with shape (N,3).
+        (fractional) Array with shape (N,3).
     """
     try:
         return np.where(displacement % 1 > 0.5, displacement % 1 - 1, displacement % 1)
@@ -57,14 +57,14 @@ def displace_positions(
     Parameters
     ----------
     positions
-        | (fractional) 2D array with shape (N,3) where N is the number of atoms.
+        (fractional) Array with shape (N,3) where N is the number of atoms.
     displacement
-        | (fractional) 2D array with shape (N,3).
+        (fractional) Array with shape (N,3).
 
     Returns
     -------
     :
-        (fractional) 2D array with shape (N,3).
+        (fractional) Array with shape (N,3).
     """
     positions = apply_pbc(positions)
     displacement = apply_pbc_displacement(displacement)
@@ -77,21 +77,23 @@ def transform_positions(
     rotation: NDArray[np.float64],
     translation: NDArray[np.float64],
 ) -> NDArray[np.float64]:
-    """Transform positions, respecting periodic boundary conditions.
+    """Transform positions.
+
+    Respects periodic boundary conditions.
 
     Parameters
     ----------
     positions
-        | (fractional) 2D array with shape (N,3) where N is the number of atoms
+        (fractional) Array with shape (N,3) where N is the number of atoms
     rotation
-        | 2D array with shape (3,3).
+        Array with shape (3,3).
     translation
-        | (fractional) 1D array with shape (3,).
+        (fractional) Array with shape (3,).
 
     Returns
     -------
     :
-        (fractional) 2D array with shape (N,3).
+        (fractional) Array with shape (N,3).
     """
     verify_positions("positions", positions)
     positions = apply_pbc(positions)
@@ -111,21 +113,21 @@ def calc_displacement(
 ) -> NDArray[np.float64]:
     """Calculate minimum displacement between two fractional positions.
 
-    Respects periodic boundary conditions.
+    Displacement is from ``positions_1`` to ``positions_2``. Respects periodic boundary
+    conditions.
 
     Parameters
     ----------
     positions_1
-        | (fractional) 2D array with shape (N,3) where N is the number of atoms.
+        (fractional) Array with shape (N,3) where N is the number of atoms.
     positions_2
-        | (fractional) 2D array with shape (N,3).
+        (fractional) Array with shape (N,3).
 
     Returns
     -------
     :
-        (fractional) 2D array with shape (N,3).
+        (fractional) Array with shape (N,3).
 
-        Displacement is from ``positions_1`` to ``positions_2``.
     """
     positions_1 = apply_pbc(positions_1)
     positions_2 = apply_pbc(positions_2)
diff --git a/ramannoodle/structure/symmetry_utils.py b/ramannoodle/structure/symmetry_utils.py
index 6ebfb24..b844e3c 100644
--- a/ramannoodle/structure/symmetry_utils.py
+++ b/ramannoodle/structure/symmetry_utils.py
@@ -11,14 +11,14 @@
 
 
 def are_collinear(vector_1: NDArray[np.float64], vector_2: NDArray[np.float64]) -> bool:
-    """Return whether or not two vectors are collinear.
+    """Check whether two vectors are collinear.
 
     Parameters
     ----------
     vector_1
-        | 1D array with shape (M,).
+        Array with shape (M,).
     vector_2
-        | 1D array with shape (M,).
+        Array with shape (M,).
 
     """
     try:
@@ -42,14 +42,14 @@ def are_collinear(vector_1: NDArray[np.float64], vector_2: NDArray[np.float64])
 def is_orthogonal_to_all(
     vector_1: NDArray[np.float64], vectors: Iterable[NDArray[np.float64]]
 ) -> int:
-    """Check whether a given vector is orthogonal to a list of others.
+    """Check whether a vector is orthogonal to a list of other vectors.
 
     Parameters
     ----------
     vector_1
-        | 1D array with shape (M,).
+        Array with shape (M,).
     vectors
-        | Iterable containing 1D arrays with shape (M,).
+        Iterable containing arrays with shape (M,).
 
     Returns
     -------
@@ -78,19 +78,19 @@ def is_orthogonal_to_all(
 def is_collinear_with_all(
     vector_1: NDArray[np.float64], vectors: Iterable[NDArray[np.float64]]
 ) -> int:
-    """Check if a given vector is collinear to a list of others.
+    """Check if a vector is collinear to a list of other vectors.
 
     Parameters
     ----------
     vector_1
-        | 1D array with shape (M,).
+        Array with shape (M,).
     vectors
-        | Iterable containing 1D arrays with shape (M,).
+        Iterable containing arrays with shape (M,).
 
     Returns
     -------
     :
-        | First index of non-collinear vector, otherwise -1.
+        First index of non-collinear vector, otherwise -1.
 
     """
     # This implementation could be made more efficient.
@@ -104,14 +104,14 @@ def is_collinear_with_all(
 def is_non_collinear_with_all(
     vector_1: NDArray[np.float64], vectors: Iterable[NDArray[np.float64]]
 ) -> int:
-    """Check if a given vector is non-collinear to a list of others.
+    """Check if a vector is non-collinear to a list of other vectors.
 
     Parameters
     ----------
     vector_1
-        | 1D array with shape (M,).
+        Array with shape (M,).
     vectors
-        | Iterable containing 1D arrays with shape (M,).
+        Iterable containing arrays with shape (M,).
 
     Returns
     -------