From 29cfde437b18dab68a08c937c7b0d3bb3d52a066 Mon Sep 17 00:00:00 2001 From: "Matthew W. Thompson" Date: Fri, 14 Aug 2026 11:15:24 -0500 Subject: [PATCH 01/26] Add examples --- .github/workflows/ci.yaml | 1 + examples/README.md | 9 + examples/compute-energy.ipynb | 348 +++++ examples/conformer-minimization.ipynb | 216 +++ examples/md-simulations.ipynb | 343 +++++ examples/parameter-gradients.ipynb | 167 +++ pixi.lock | 1905 ++++++++++++++++++++++++- pixi.toml | 4 + 8 files changed, 2989 insertions(+), 4 deletions(-) create mode 100644 examples/README.md create mode 100644 examples/compute-energy.ipynb create mode 100644 examples/conformer-minimization.ipynb create mode 100644 examples/md-simulations.ipynb create mode 100644 examples/parameter-gradients.ipynb diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 6a48169..c4c994f 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -25,6 +25,7 @@ jobs: base: - base - mm + - examples steps: - uses: actions/checkout@v7 diff --git a/examples/README.md b/examples/README.md new file mode 100644 index 0000000..83377f5 --- /dev/null +++ b/examples/README.md @@ -0,0 +1,9 @@ +# Examples + +This directory contains a number of examples of how to use `fitlib`. They currently include: + +* [Evaluating the energy of a water dimer with virtual sites](compute-energy.ipynb) +* [Minimizing the conformer of a molecule](conformer-minimization.ipynb) +* [Computing the gradient of the energy w.r.t. force field parameters](parameter-gradients.ipynb) +* [Registering custom parameter handlers](custom-handler.ipynb) +* [Differentiably compute ensemble averages from MD simulations](md-simulations.ipynb) diff --git a/examples/compute-energy.ipynb b/examples/compute-energy.ipynb new file mode 100644 index 0000000..ac94a5c --- /dev/null +++ b/examples/compute-energy.ipynb @@ -0,0 +1,348 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Computing energies using `fitlib`\n", + "\n", + "This example will show how the energy of a water dimer (TIP4P-FB and TIP3P) in multiple conformations can be evaluated using `fitlib` framework. \n", + "\n", + "We start by creating the objects that will store our parameterized water dimer. First we will create a single water molecule, and parameterize it using OpenFF Interchange:" + ], + "metadata": { + "collapsed": false + }, + "id": "25a78d447fc68708" + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [ + { + "data": { + "text/plain": "", + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "2bb574b6b3e04c1692c9a07bbcc9518f" + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import openff.toolkit\n", + "import openff.units\n", + "import torch\n", + "\n", + "water = openff.toolkit.Molecule.from_smiles(\"O\")\n", + "water.generate_conformers(n_conformers=1)\n", + "\n", + "import openff.interchange\n", + "\n", + "tip4p_interchange = openff.interchange.Interchange.from_smirnoff(\n", + " openff.toolkit.ForceField(\"tip4p_fb.offxml\"), water.to_topology()\n", + ")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-17T21:18:41.279805Z", + "start_time": "2023-10-17T21:18:38.097654Z" + } + }, + "id": "90bd4bbb3c3dbf19" + }, + { + "cell_type": "markdown", + "source": [ + "The interchange must then be mapped into collections of tensors:" + ], + "metadata": { + "collapsed": false + }, + "id": "5894a5eec986e7cf" + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "import fitlib.converters\n", + "\n", + "tip4p_tensor_ff, [tip4p_topology] = fitlib.converters.convert_interchange(\n", + " tip4p_interchange\n", + ")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-17T21:18:41.447289Z", + "start_time": "2023-10-17T21:18:41.282553Z" + } + }, + "id": "e269db087ca0015" + }, + { + "cell_type": "markdown", + "source": [ + "**Note:** The `convert_interchange` function can take either a single interchange object, or a list of multiple.\n", + "\n", + "These tensors are returned as:\n", + "\n", + "* a `fitlib.ff.TensorForceField` oject: this stores the original values of the force field parameters in tensor form.\n", + "* a list of ``fitlib.ff.TensorTopology`` objects: each 'topology' will store basic information about each input molecule, in addition to matrices defining which parameters were assigned to which elements (e.g. bonds, angles, torsions) and any virtual sites that were added.\n", + "\n", + "To define fuller systems (e.g. dimers, condensed phase systems etc), we need to wrap the topologies in a system object:" + ], + "metadata": { + "collapsed": false + }, + "id": "ee2d99f7f39f164b" + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "tip4p_dimer = fitlib.TensorSystem([tip4p_topology], n_copies=[2], is_periodic=False)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-17T21:18:41.452055Z", + "start_time": "2023-10-17T21:18:41.449087Z" + } + }, + "id": "5f15e066ec067e2" + }, + { + "cell_type": "markdown", + "source": [ + "This mirrors how GROMACS topology files are defined, whereby individual species are parameterised and then a count of how many of each should be present is specified. This avoids the need to store multiple copies of the same parameters for each copy of a given molecule.\n", + "\n", + "We can then define the coordinates of our dimers:" + ], + "metadata": { + "collapsed": false + }, + "id": "8a5e68185c0e86ce" + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [ + { + "data": { + "text/plain": "torch.Size([40, 8, 3])" + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "water_conformer = torch.tensor(water.conformers[0].m_as(openff.units.unit.angstrom))\n", + "tip4p_conformer = fitlib.add_v_site_coords(\n", + " tip4p_topology.v_sites, water_conformer, tip4p_tensor_ff\n", + ")\n", + "\n", + "tip4p_conformers = torch.stack(\n", + " [\n", + " torch.vstack([tip4p_conformer, tip4p_conformer + torch.tensor(1.5 + i * 0.05)])\n", + " for i in range(40)\n", + " ]\n", + ")\n", + "tip4p_conformers.shape" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-17T21:18:41.467461Z", + "start_time": "2023-10-17T21:18:41.453597Z" + } + }, + "id": "ed6917e11f537604" + }, + { + "cell_type": "markdown", + "source": [ + "For simplicity, we have created 40 dimer conformers, with a separation ranging from 1.5-3.5 Å by crudely shifting our initial conformer along the x-axis. \n", + "\n", + "**Note:** Conformers should either be tensor with a shape of `(n_particles, 3)` or `(n_conformers, n_particles, 3)`, and units of Å._\n", + "\n", + "From the shape we can clearly see there are 40 conformers, each with coordinates for 8 particles (i.e. 3 for each water molecule + 1 virtual site on each)." + ], + "metadata": { + "collapsed": false + }, + "id": "64dec87dff8bd042" + }, + { + "cell_type": "markdown", + "source": [ + "The energy each dimer can then be directly evaluated and plotted" + ], + "metadata": { + "collapsed": false + }, + "id": "a08299ce29b22a20" + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [ + { + "data": { + "text/plain": "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tip4p_energies = fitlib.compute_energy(tip4p_dimer, tip4p_tensor_ff, tip4p_conformers)\n", + "\n", + "from matplotlib import pyplot\n", + "\n", + "pyplot.plot(tip4p_energies, label=\"tip4p\")\n", + "pyplot.ylabel(\"Energy [kcal / mol]\");" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-17T21:18:41.786176Z", + "start_time": "2023-10-17T21:18:41.464803Z" + } + }, + "id": "2ac831ea0b442177" + }, + { + "cell_type": "markdown", + "source": [ + "The above can easily be repeated using the TIP3P water model, allowing us to compare the dimer energy curves." + ], + "metadata": { + "collapsed": false + }, + "id": "ad55d105cc532449" + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tip3p_interchange = openff.interchange.Interchange.from_smirnoff(\n", + " openff.toolkit.ForceField(\"tip3p.offxml\"), water.to_topology()\n", + ")\n", + "\n", + "tip3p_tensor_ff, [tip3p_tensor_topology] = fitlib.converters.convert_interchange(\n", + " tip3p_interchange\n", + ")\n", + "tip3p_dimer = fitlib.TensorSystem(\n", + " [tip3p_tensor_topology], n_copies=[2], is_periodic=False\n", + ")\n", + "\n", + "tip3p_conformers = torch.stack(\n", + " [\n", + " torch.vstack([water_conformer, water_conformer + torch.tensor(1.5 + i * 0.05)])\n", + " for i in range(40)\n", + " ]\n", + ")\n", + "\n", + "tip3p_energies = fitlib.compute_energy(tip3p_dimer, tip3p_tensor_ff, tip3p_conformers)\n", + "\n", + "pyplot.plot(tip3p_energies, label=\"tip3p\")\n", + "pyplot.plot(tip4p_energies, label=\"tip4p\")\n", + "\n", + "pyplot.ylabel(\"Energy [kcal / mol]\")\n", + "pyplot.legend();" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-17T21:18:41.963940Z", + "start_time": "2023-10-17T21:18:41.789077Z" + } + }, + "id": "f15d2ad0a937d942" + }, + { + "cell_type": "markdown", + "source": [ + "A further advantage of storing our applied force field parameters in fitlib form is the ability to compute only the energy due to a particular handler (e.g. only LJ energies). " + ], + "metadata": { + "collapsed": false + }, + "id": "35f892abe7da6eb3" + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tip3p_lj_energies = fitlib.compute_energy_potential(\n", + " tip3p_dimer, tip3p_tensor_ff.potentials_by_type[\"vdW\"], tip3p_conformers\n", + ")\n", + "tip4p_lj_energies = fitlib.compute_energy_potential(\n", + " tip4p_dimer, tip4p_tensor_ff.potentials_by_type[\"vdW\"], tip4p_conformers\n", + ")\n", + "\n", + "pyplot.plot(tip3p_lj_energies, label=\"tip3p\")\n", + "pyplot.plot(tip4p_lj_energies, label=\"tip4p\")\n", + "\n", + "pyplot.ylabel(\"LJ Energy [kcal / mol]\");" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-17T21:18:42.085373Z", + "start_time": "2023-10-17T21:18:41.965387Z" + } + }, + "id": "b24b1cbe4a52d794" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/conformer-minimization.ipynb b/examples/conformer-minimization.ipynb new file mode 100644 index 0000000..1ae365a --- /dev/null +++ b/examples/conformer-minimization.ipynb @@ -0,0 +1,216 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "261b79c7042b8a6f", + "metadata": { + "collapsed": false + }, + "source": [ + "# Conformer Minimization\n", + "\n", + "This example will show how to optimize a conformer of paracetamol.\n", + "\n", + "Load in a paracetamol molecule, generate a conformer for it, and perturb the conformer to ensure it needs minimization." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b081ee3aecf864ac", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T21:18:13.134692Z", + "start_time": "2023-10-17T21:18:10.562001Z" + }, + "collapsed": false + }, + "outputs": [], + "source": [ + "import openff.toolkit\n", + "import openff.units\n", + "import torch\n", + "\n", + "molecule = openff.toolkit.Molecule.from_smiles(\"CC(=O)NC1=CC=C(C=C1)O\")\n", + "molecule.generate_conformers(n_conformers=1)\n", + "\n", + "conformer = torch.tensor(molecule.conformers[0].m_as(openff.units.unit.angstrom)) * 1.10\n", + "conformer.requires_grad = True" + ] + }, + { + "cell_type": "markdown", + "id": "f4168aec7a72494c", + "metadata": { + "collapsed": false + }, + "source": [ + "We specify that the gradient of the conformer is required so that we can optimize it using PyTorch.\n", + "\n", + "Parameterize the molecule using OpenFF Interchange and convert it into a PyTorch tensor representation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8d00fd2dcf4c27cf", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T21:18:16.758187Z", + "start_time": "2023-10-17T21:18:13.138018Z" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d8c8c3f62d1448a4b07498d18cf6dc5f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import openff.interchange\n", + "\n", + "interchange = openff.interchange.Interchange.from_smirnoff(\n", + " openff.toolkit.ForceField(\"openff-2.3.0.offxml\"),\n", + " molecule.to_topology(),\n", + ")\n", + "\n", + "import fitlib.converters\n", + "\n", + "force_field, [topology] = fitlib.converters.convert_interchange(interchange)" + ] + }, + { + "cell_type": "markdown", + "id": "792cb057cb419fa8", + "metadata": { + "collapsed": false + }, + "source": [ + "We can minimize the conformer using any of PyTorch's optimizers. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "facd656a27cf46a8", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T21:18:17.036136Z", + "start_time": "2023-10-17T21:18:16.761394Z" + }, + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0: E=102.10968017578125 kcal / mol\n", + "Epoch 5: E=7.088213920593262 kcal / mol\n", + "Epoch 10: E=-18.331130981445312 kcal / mol\n", + "Epoch 15: E=-22.182296752929688 kcal / mol\n", + "Epoch 20: E=-30.369152069091797 kcal / mol\n", + "Epoch 25: E=-36.81045150756836 kcal / mol\n", + "Epoch 30: E=-38.517852783203125 kcal / mol\n", + "Epoch 35: E=-40.50505828857422 kcal / mol\n", + "Epoch 40: E=-42.08476257324219 kcal / mol\n", + "Epoch 45: E=-42.19199752807617 kcal / mol\n", + "Epoch 50: E=-42.37827682495117 kcal / mol\n", + "Epoch 55: E=-42.6767692565918 kcal / mol\n", + "Epoch 60: E=-42.799903869628906 kcal / mol\n", + "Epoch 65: E=-42.94251251220703 kcal / mol\n", + "Epoch 70: E=-43.037200927734375 kcal / mol\n", + "Epoch 74: E=-43.084136962890625 kcal / mol\n" + ] + } + ], + "source": [ + "import fitlib\n", + "\n", + "optimizer = torch.optim.Adam([conformer], lr=0.02)\n", + "\n", + "for epoch in range(75):\n", + " energy = fitlib.compute_energy(topology, force_field, conformer)\n", + " energy.backward()\n", + "\n", + " optimizer.step()\n", + " optimizer.zero_grad()\n", + "\n", + " if epoch % 5 == 0 or epoch == 74:\n", + " print(f\"Epoch {epoch}: E={energy.item()} kcal / mol\")" + ] + }, + { + "cell_type": "markdown", + "id": "360d6eb9cf2b6cc4", + "metadata": { + "collapsed": false + }, + "source": [ + "We can then re-store the optimized conformer back into the molecule. Here we add the conformer to the molecule's conformer list, but we could also replace the original conformer." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "eaec04c4039ca59b", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T21:18:17.052947Z", + "start_time": "2023-10-17T21:18:17.036498Z" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "449fcae6d9eb4e5a8a3d765f0608e399", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "NGLWidget(max_frame=1)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "molecule.add_conformer(conformer.detach().numpy() * openff.units.unit.angstrom)\n", + "molecule.visualize(backend=\"nglview\")" + ] + } + ], + "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": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/md-simulations.ipynb b/examples/md-simulations.ipynb new file mode 100644 index 0000000..71fed4b --- /dev/null +++ b/examples/md-simulations.ipynb @@ -0,0 +1,343 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "aaf952cb344ca32d", + "metadata": { + "collapsed": false + }, + "source": [ + "# Ensemble Averages from MD Simulations\n", + "\n", + "This example shows how ensemble averages can be computed from MD simulations, such that their gradient with respect to force field parameters can be computed through backpropagation.\n", + "\n", + "We start by parameterizing the set of molecules that will appear in our simulation boxes: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e178932166969df5", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import openff.interchange\n", + "import openff.toolkit\n", + "\n", + "import fitlib.converters\n", + "\n", + "interchanges = [\n", + " openff.interchange.Interchange.from_smirnoff(\n", + " openff.toolkit.ForceField(\"openff-2.3.0.offxml\"),\n", + " openff.toolkit.Molecule.from_smiles(smiles).to_topology(),\n", + " )\n", + " for smiles in (\"CCO\", \"CO\")\n", + "]\n", + "\n", + "tensor_ff, topologies = fitlib.converters.convert_interchange(interchanges)\n", + "\n", + "# move the force field to the GPU for faster processing of the simulation\n", + "# trajectories - the system and force field must be on the same device.\n", + "tensor_ff = tensor_ff.to(\"cuda\")" + ] + }, + { + "cell_type": "markdown", + "id": "415cbefaa3d60c49", + "metadata": { + "collapsed": false + }, + "source": [ + "We will also flag that the vdW parameter gradients are required:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38700a4d251b1ab9", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "vdw_potential = tensor_ff.potentials_by_type[\"vdW\"]\n", + "vdw_potential.parameters.requires_grad = True" + ] + }, + { + "cell_type": "markdown", + "id": "824dfd3f7f6916b3", + "metadata": { + "collapsed": false + }, + "source": [ + "We then define the full simulation boxes that we wish to simulate:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "58a584bf7997e194", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import fitlib\n", + "\n", + "# define a periodic box containing 216 ethanol molecules\n", + "system_ethanol = fitlib.TensorSystem([topologies[0]], [216], is_periodic=True)\n", + "system_ethanol = system_ethanol.to(\"cuda\")\n", + "# define a periodic box containing 216 methanol molecules\n", + "system_methanol = fitlib.TensorSystem([topologies[1]], [216], True)\n", + "system_methanol = system_methanol.to(\"cuda\")\n", + "# define a periodic box containing 128 ethanol molecules and 128 methanol molecules\n", + "system_mixture = fitlib.TensorSystem(topologies, [128, 128], True)\n", + "system_mixture = system_mixture.to(\"cuda\")" + ] + }, + { + "cell_type": "markdown", + "id": "93a519affcb22db4", + "metadata": { + "collapsed": false + }, + "source": [ + "A tensor system is simply a wrapper around a set of topology objects that define parameters applied to individual molecules, and the number of copies of that topology that should be present similar to GROMACS topologies. The `is_periodic` flag indicates whether the system should be simulated in a periodic box.\n", + "\n", + "Here we have also moved the systems onto the GPU. This will allow us to much more rapidly compute ensemble averages from the trajectories, but is not required.\n", + "\n", + "We then also must define the simulation protocol that will be used to run the simulations. This consists of a config object that defines how to generate the system coordinates using PACKMOL, the set of energy minimisations /simulations to run as equilibration, and finally the configuration of the production simulation:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ccba93245cf83ff7", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import tempfile\n", + "\n", + "import openmm.unit\n", + "\n", + "import fitlib.mm\n", + "\n", + "temperature = 298.15 * openmm.unit.kelvin\n", + "pressure = 1.0 * openmm.unit.atmosphere\n", + "\n", + "beta = 1.0 / (openmm.unit.MOLAR_GAS_CONSTANT_R * temperature)\n", + "\n", + "# we can run an arbitrary number of equilibration simulations / minimizations.\n", + "# all generated data will be discarded, but the final coordinates will be used\n", + "# to initialize the production simulation\n", + "equilibrate_config = [\n", + " fitlib.mm.MinimizationConfig(),\n", + " # short NVT equilibration simulation\n", + " fitlib.mm.SimulationConfig(\n", + " temperature=temperature,\n", + " pressure=None,\n", + " n_steps=50000,\n", + " timestep=1.0 * openmm.unit.femtosecond,\n", + " ),\n", + " # short NPT equilibration simulation\n", + " fitlib.mm.SimulationConfig(\n", + " temperature=temperature,\n", + " pressure=pressure,\n", + " n_steps=50000,\n", + " timestep=1.0 * openmm.unit.femtosecond,\n", + " ),\n", + "]\n", + "# long NPT production simulation\n", + "production_config = fitlib.mm.SimulationConfig(\n", + " temperature=temperature,\n", + " pressure=pressure,\n", + " n_steps=500000,\n", + " timestep=2.0 * openmm.unit.femtosecond,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9db4088f839c4265", + "metadata": { + "collapsed": false + }, + "source": [ + "We will further define a convenience function that will first simulate the system of interest (storing the trajectory in a temporary directory), and then compute ensemble averages over that trajectory:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d485cce5c1a0fce3", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pathlib\n", + "\n", + "import torch\n", + "\n", + "\n", + "def compute_ensemble_averages(\n", + " system: fitlib.TensorSystem, force_field: fitlib.TensorForceField\n", + ") -> dict[str, torch.Tensor]:\n", + " # computing the ensemble averages is a two step process - we first need to run\n", + " # an MD simulation using the force field making sure to store the coordinates,\n", + " # box vectors and kinetic energies\n", + " coords, box_vectors = fitlib.mm.generate_system_coords(system, force_field)\n", + "\n", + " interval = 1000\n", + "\n", + " # save the simulation output every 1000th frame (2 ps) to a temporary file.\n", + " # we could also save the trajectory more permanently, but as we do nothing\n", + " # with it after computing the averages in this example, we simply want to\n", + " # discard it.\n", + " with (\n", + " tempfile.NamedTemporaryFile() as tmp_file,\n", + " fitlib.mm.tensor_reporter(tmp_file.name, interval, beta, pressure) as reporter,\n", + " ):\n", + " fitlib.mm.simulate(\n", + " system,\n", + " force_field,\n", + " coords,\n", + " box_vectors,\n", + " equilibrate_config,\n", + " production_config,\n", + " [reporter],\n", + " )\n", + "\n", + " # we can then compute the ensemble averages from the trajectory. generating\n", + " # the trajectory separately from computing the ensemble averages allows us\n", + " # to run the simulation in parallel with other simulations more easily, without\n", + " # having to worry about copying gradients between workers / processes.\n", + " avgs, stds = fitlib.mm.compute_ensemble_averages(\n", + " system, force_field, pathlib.Path(tmp_file.name), temperature, pressure\n", + " )\n", + " return avgs" + ] + }, + { + "cell_type": "markdown", + "id": "8d1fdb6973324d14", + "metadata": { + "collapsed": false + }, + "source": [ + "Computing the ensemble averages is then as simple as:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3156bcfc509380f7", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# run simulations of each system and compute ensemble averages over the trajectories\n", + "# of the potential energy, volume, and density\n", + "ethanol_avgs = compute_ensemble_averages(system_ethanol, tensor_ff)\n", + "methanol_avgs = compute_ensemble_averages(system_methanol, tensor_ff)\n", + "mixture_avgs = compute_ensemble_averages(system_mixture, tensor_ff)" + ] + }, + { + "cell_type": "markdown", + "id": "bcce5c83a564c59f", + "metadata": { + "collapsed": false + }, + "source": [ + "Each of the returned values is a dictionary of ensemble averages computed over the simulated production trajectory. This currently includes the potential energy, volume, and density of the system.\n", + "\n", + "These averages can be used in a loss function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38b9a27d7cd06c1a", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# define some MOCK data and loss function\n", + "mock_ethanol_density = 0.789 # g/mL\n", + "mock_methanol_density = 0.791 # g/mL\n", + "\n", + "mock_enthalpy_of_mixing = 0.891 # kcal/mol\n", + "\n", + "loss = (ethanol_avgs[\"density\"] - mock_ethanol_density) ** 2\n", + "loss += (methanol_avgs[\"density\"] - mock_methanol_density) ** 2\n", + "\n", + "mixture_enthalpy = mixture_avgs[\"enthalpy\"] / 256\n", + "\n", + "ethanol_enthalpy = ethanol_avgs[\"enthalpy\"] / 128\n", + "methanol_enthalpy = methanol_avgs[\"enthalpy\"] / 128\n", + "\n", + "enthalpy_of_mixing = mixture_enthalpy - (\n", + " 0.5 * ethanol_enthalpy + 0.5 * methanol_enthalpy\n", + ")\n", + "loss += (enthalpy_of_mixing - mock_enthalpy_of_mixing) ** 2" + ] + }, + { + "cell_type": "markdown", + "id": "9bd6779012316898", + "metadata": { + "collapsed": false + }, + "source": [ + "and the gradient of this loss function with respect to the force field parameters can be computed through backpropagation:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd3ccdfe61a0cd09", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "loss.backward()\n", + "\n", + "epsilon_col = vdw_potential.parameter_cols.index(\"epsilon\")\n", + "sigma_col = vdw_potential.parameter_cols.index(\"sigma\")\n", + "\n", + "print(\"VdW Ɛ Gradients\", vdw_potential.parameters.grad[:, epsilon_col])\n", + "print(\"VdW σ Gradients\", vdw_potential.parameters.grad[:, sigma_col])" + ] + } + ], + "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": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/parameter-gradients.ipynb b/examples/parameter-gradients.ipynb new file mode 100644 index 0000000..54babc6 --- /dev/null +++ b/examples/parameter-gradients.ipynb @@ -0,0 +1,167 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Parameter Gradients\n", + "\n", + "This example will show how the gradient of the potential energy with respect to force field parameters may be computed.\n", + "\n", + "We start be loading and parameterizing the molecule of interest." + ], + "metadata": { + "collapsed": false + }, + "id": "581674729871c21a" + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [ + { + "data": { + "text/plain": "", + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "9f2762d7fdec4691a8fba35cac8e7f98" + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import openff.interchange\n", + "import openff.toolkit\n", + "import openff.units\n", + "import torch\n", + "\n", + "import fitlib.converters\n", + "\n", + "molecule = openff.toolkit.Molecule.from_smiles(\"CC(=O)NC1=CC=C(C=C1)O\")\n", + "molecule.generate_conformers(n_conformers=1)\n", + "\n", + "conformer = torch.tensor(molecule.conformers[0].m_as(openff.units.unit.angstrom))\n", + "\n", + "interchange = openff.interchange.Interchange.from_smirnoff(\n", + " openff.toolkit.ForceField(\"openff_unconstrained-2.3.0.offxml\"),\n", + " molecule.to_topology(),\n", + ")\n", + "tensor_ff, [tensor_topology] = fitlib.converters.convert_interchange(interchange)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-17T21:17:56.406479Z", + "start_time": "2023-10-17T21:17:50.319630Z" + } + }, + "id": "67b29771d6b77bc" + }, + { + "cell_type": "markdown", + "source": [ + "We can access the parameters for each SMIRNOFF parameter 'handler' (e.g. vdW, bond, angle, etc.) using the `potentials_by_type` (or the `potentials`) attribute of the `TensorForceField` object." + ], + "metadata": { + "collapsed": false + }, + "id": "338809852105c2d8" + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "vdw_potential = tensor_ff.potentials_by_type[\"vdW\"]\n", + "vdw_potential.parameters.requires_grad = True" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-17T21:17:56.416841Z", + "start_time": "2023-10-17T21:17:56.409028Z" + } + }, + "id": "4757416c61bdcf8d" + }, + { + "cell_type": "markdown", + "source": [ + "The gradient of the potential energy with respect to the parameters can then be computed by backpropagating through the energy computation." + ], + "metadata": { + "collapsed": false + }, + "id": "6e8d221ef30f9da6" + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[#6X4:1] - dU/depsilon = -1.033, dU/dsigma = -0.139\n", + "[#6:1] - dU/depsilon = 87.490, dU/dsigma = 34.983\n", + "[#8:1] - dU/depsilon = 15.846, dU/dsigma = 17.232\n", + "[#7:1] - dU/depsilon = 0.148, dU/dsigma = 1.187\n", + "[#8X2H1+0:1] - dU/depsilon = -0.305, dU/dsigma = 0.558\n", + "[#1:1]-[#6X4] - dU/depsilon = 7.630, dU/dsigma = 1.404\n", + "[#1:1]-[#7] - dU/depsilon = -2.894, dU/dsigma = -0.074\n", + "[#1:1]-[#6X3] - dU/depsilon = 137.134, dU/dsigma = 12.129\n", + "[#1:1]-[#8] - dU/depsilon = -22.417, dU/dsigma = -0.001\n" + ] + } + ], + "source": [ + "import fitlib\n", + "\n", + "energy = fitlib.compute_energy(tensor_topology, tensor_ff, conformer)\n", + "energy.backward()\n", + "\n", + "for parameter_key, gradient in zip(\n", + " vdw_potential.parameter_keys, vdw_potential.parameters.grad.numpy(), strict=True\n", + "):\n", + " parameter_cols = vdw_potential.parameter_cols\n", + "\n", + " parameter_grads = \", \".join(\n", + " f\"dU/d{parameter_col} = {parameter_grad: 8.3f}\"\n", + " for parameter_col, parameter_grad in zip(parameter_cols, gradient, strict=True)\n", + " )\n", + " print(f\"{parameter_key.id.ljust(15)} - {parameter_grads}\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-17T21:17:56.452896Z", + "start_time": "2023-10-17T21:17:56.415831Z" + } + }, + "id": "6df321d552be0aad" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pixi.lock b/pixi.lock index 4dbd69c..6689a32 100644 --- a/pixi.lock +++ b/pixi.lock @@ -4091,6 +4091,781 @@ environments: - pypi: https://files.pythonhosted.org/packages/a4/62/02da182e544a51a5c3ccf4b03ab79df279f9c60c5e82d5e8bec7ca26ac11/python_slugify-8.0.4-py2.py3-none-any.whl - pypi: https://files.pythonhosted.org/packages/a6/a5/c0b6468d3824fe3fde30dbb5e1f687b291608f9473681bbf7dabbf5a87d7/text_unidecode-1.3-py2.py3-none-any.whl - 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Thompson" Date: Fri, 14 Aug 2026 11:17:27 -0500 Subject: [PATCH 02/26] Update lockfile --- pixi.lock | 4799 ++++++++++++++++++++++++----------------------------- 1 file changed, 2138 insertions(+), 2661 deletions(-) diff --git a/pixi.lock b/pixi.lock index 6689a32..dc23c27 100644 --- a/pixi.lock +++ b/pixi.lock @@ -46,13 +46,13 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/backports.zstd-1.6.0-py312h90b7ffd_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/biopython-1.88-py312h4c3975b_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/blosc-1.21.6-he440d0b_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-1.2.0-hed03a55_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.2.0-hb03c661_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.2.0-py312hdb49522_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-1.2.0-h505cf86_3.conda + - 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Thompson" Date: Fri, 14 Aug 2026 12:59:29 -0500 Subject: [PATCH 06/26] Do not fail fast --- .github/workflows/ci.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index c4c994f..1fe8609 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -18,6 +18,7 @@ jobs: PIXI_ENV: ${{ matrix.base }}-py${{ matrix.python-version }} strategy: + fail-fast: false matrix: os: [ubuntu-latest, macos-latest] python-version: ["312", "313"] From edb38bd7a2db7f5ae724fb9d7d34a850cfd7f00c Mon Sep 17 00:00:00 2001 From: "Matthew W. Thompson" Date: Fri, 14 Aug 2026 13:25:55 -0500 Subject: [PATCH 07/26] Shuffle dependencies --- fitlib/_tests/potentials/test_potentials.py | 2 ++ pixi.lock | 8 ++++++++ pixi.toml | 3 +-- 3 files changed, 11 insertions(+), 2 deletions(-) diff --git a/fitlib/_tests/potentials/test_potentials.py b/fitlib/_tests/potentials/test_potentials.py index b4ea49c..904bd16 100644 --- a/fitlib/_tests/potentials/test_potentials.py +++ b/fitlib/_tests/potentials/test_potentials.py @@ -243,6 +243,8 @@ def test_compute_energy_v_sites(): @pytest.mark.parametrize("periodic", [True, False]) def test_energy_backward_pass(periodic): + import fitlib.mm + tensor_sys, tensor_ff = fitlib._tests.utils.system_from_smiles(["CCC", "O"], [2, 3]) tensor_sys.is_periodic = periodic diff --git a/pixi.lock b/pixi.lock index 65b2819..6a6f44f 100644 --- a/pixi.lock +++ b/pixi.lock @@ -386,6 +386,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/pybind11-global-3.0.4-pyh648e204_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-3.0-pyhcf101f3_0.conda - 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Thompson" Date: Mon, 24 Aug 2026 11:01:26 -0500 Subject: [PATCH 09/26] Debug --- .github/workflows/ci.yaml | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 1fe8609..25cf83e 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -18,7 +18,7 @@ jobs: PIXI_ENV: ${{ matrix.base }}-py${{ matrix.python-version }} strategy: - fail-fast: false + fail-fast: true matrix: os: [ubuntu-latest, macos-latest] python-version: ["312", "313"] @@ -33,8 +33,10 @@ jobs: - name: Write OE license file env: - OE_LICENSE_TEXT: ${{ secrets.OE_LICENSE }} - run: echo "${OE_LICENSE_TEXT}" > "${OE_LICENSE}" + SECRET_OE_LICENSE: ${{ secrets.OE_LICENSE }} + run: | + echo "${SECRET_OE_LICENSE}" > ${OE_LICENSE} + pixi run -e ${{ matrix.environment }} python -c "from openeye import oechem; assert oechem.OEChemIsLicensed()" - name: Setup virtual environment uses: prefix-dev/setup-pixi@v0.10.1 From 25541fa9b8130e9bb2ad7f8ce1a44b4b0237b2d6 Mon Sep 17 00:00:00 2001 From: "Matthew W. Thompson" Date: Mon, 24 Aug 2026 11:03:54 -0500 Subject: [PATCH 10/26] Debug --- .github/workflows/ci.yaml | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 25cf83e..24f6182 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -31,6 +31,12 @@ jobs: steps: - uses: actions/checkout@v7 + - name: Setup virtual environment + uses: prefix-dev/setup-pixi@v0.10.1 + with: + cache: true + environments: ${{ env.PIXI_ENV }} + - name: Write OE license file env: SECRET_OE_LICENSE: ${{ secrets.OE_LICENSE }} @@ -38,12 +44,6 @@ jobs: echo "${SECRET_OE_LICENSE}" > ${OE_LICENSE} pixi run -e ${{ matrix.environment }} python -c "from openeye import oechem; assert oechem.OEChemIsLicensed()" - - name: Setup virtual environment - uses: prefix-dev/setup-pixi@v0.10.1 - with: - cache: true - environments: ${{ env.PIXI_ENV }} - - name: Install and license OpenEye Toolkits if: false run: | From a696c003777b6291fd34af34a9ce6393ee8b95bc Mon Sep 17 00:00:00 2001 From: "Matthew W. Thompson" Date: Mon, 24 Aug 2026 11:07:27 -0500 Subject: [PATCH 11/26] Debug --- .github/workflows/ci.yaml | 10 +--------- 1 file changed, 1 insertion(+), 9 deletions(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 24f6182..c666bc9 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -42,15 +42,7 @@ jobs: SECRET_OE_LICENSE: ${{ secrets.OE_LICENSE }} run: | echo "${SECRET_OE_LICENSE}" > ${OE_LICENSE} - pixi run -e ${{ matrix.environment }} python -c "from openeye import oechem; assert oechem.OEChemIsLicensed()" - - - name: Install and license OpenEye Toolkits - if: false - run: | - echo "${SECRET_OE_LICENSE}" > ${OE_LICENSE} - pixi run -e ${{ matrix.environment }} python -c "from openeye import oechem; assert oechem.OEChemIsLicensed()" - env: - SECRET_OE_LICENSE: ${{ secrets.OE_LICENSE }} + pixi run -e "${{ env.PIXI_ENV }}" python -c "from openeye import oechem; assert oechem.OEChemIsLicensed()" - name: Run type-checker run: pixi run -e "${{ env.PIXI_ENV }}" type-check From a7462b189e501a4822416f34fd3c8ea10c0a224f Mon Sep 17 00:00:00 2001 From: "Matthew W. Thompson" Date: Mon, 24 Aug 2026 11:19:07 -0500 Subject: [PATCH 12/26] Debug --- .github/workflows/ci.yaml | 1 + pixi.toml | 4 ++-- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index c666bc9..826b324 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -38,6 +38,7 @@ jobs: environments: ${{ env.PIXI_ENV }} - name: Write OE license file + if: ${{ !contains(env.PIXI_ENV, 'docs') }} env: SECRET_OE_LICENSE: ${{ secrets.OE_LICENSE }} run: | diff --git a/pixi.toml b/pixi.toml index 25c88b2..d7e7b00 100644 --- a/pixi.toml +++ b/pixi.toml @@ -126,8 +126,8 @@ types-PyYAML = "*" # "base" environments need something for pre-AshGC force fields, openeye is vastly easier to install base-py312 = ["py312", "openeye", "ambertools", "test", "typing"] base-py313 = ["py313", "openeye", "ambertools", "test", "typing"] -mm-py312 = ["py312", "ambertools", "mm", "test", "typing"] -mm-py313 = ["py313", "ambertools", "mm", "test", "typing"] +mm-py312 = ["py312", "openeye", "ambertools", "mm", "test", "typing"] +mm-py313 = ["py313", "openeye", "ambertools", "mm", "test", "typing"] examples-py312 = ["py312", "mm", "openeye", "examples", "test", "typing"] examples-py313 = ["py313", "mm", "openeye", "examples", "test", "typing"] dev = ["py312", "mm", "openeye", "test", "typing", "dev"] From ca12ca546d9c3e9f068a793ca67651837ad8ccd2 Mon Sep 17 00:00:00 2001 From: "Matthew W. Thompson" Date: Mon, 24 Aug 2026 11:21:21 -0500 Subject: [PATCH 13/26] Update lockfile --- pixi.lock | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/pixi.lock b/pixi.lock index 6735883..0c18e64 100644 --- a/pixi.lock +++ b/pixi.lock @@ -5996,6 +5996,7 @@ environments: mm-py312: channels: - url: https://conda.anaconda.org/conda-forge/ + - url: https://conda.anaconda.org/openeye/ indexes: - https://pypi.org/simple packages: @@ -6430,6 +6431,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/xmltodict-1.0.4-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/xyzservices-2026.3.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-4.1.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/openeye/linux-64/openeye-toolkits-2026.1.0-py312_0.conda - pypi: ./ - pypi: https://files.pythonhosted.org/packages/08/52/eefeba09be4ef2a1eb989eb92934561e8e502a6ee3c32654996e4be7e399/types_pyyaml-6.0.12.20260815-py3-none-any.whl - pypi: https://files.pythonhosted.org/packages/12/11/263ced30149fbede00614a11f01de792842d3636b5a9e3abfee34e764a0e/scipy_stubs-1.18.1.0-py3-none-any.whl @@ -6866,6 +6868,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-1.3.2-h8088a28_3.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-ng-2.3.3-h31dac16_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.7-hf451053_7.conda + - conda: https://conda.anaconda.org/openeye/osx-arm64/openeye-toolkits-2026.1.0-py312_0.conda - pypi: ./ - pypi: https://files.pythonhosted.org/packages/08/52/eefeba09be4ef2a1eb989eb92934561e8e502a6ee3c32654996e4be7e399/types_pyyaml-6.0.12.20260815-py3-none-any.whl - pypi: https://files.pythonhosted.org/packages/12/11/263ced30149fbede00614a11f01de792842d3636b5a9e3abfee34e764a0e/scipy_stubs-1.18.1.0-py3-none-any.whl @@ -6885,6 +6888,7 @@ environments: mm-py313: channels: - url: https://conda.anaconda.org/conda-forge/ + - url: https://conda.anaconda.org/openeye/ indexes: - https://pypi.org/simple packages: @@ -7317,6 +7321,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/xmltodict-1.0.4-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/xyzservices-2026.3.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-4.1.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/openeye/linux-64/openeye-toolkits-2026.1.0-py313_0.conda - pypi: ./ - pypi: https://files.pythonhosted.org/packages/08/52/eefeba09be4ef2a1eb989eb92934561e8e502a6ee3c32654996e4be7e399/types_pyyaml-6.0.12.20260815-py3-none-any.whl - pypi: https://files.pythonhosted.org/packages/12/11/263ced30149fbede00614a11f01de792842d3636b5a9e3abfee34e764a0e/scipy_stubs-1.18.1.0-py3-none-any.whl @@ -7753,6 +7758,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-1.3.2-h8088a28_3.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-ng-2.3.3-h31dac16_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.7-hf451053_7.conda + - conda: https://conda.anaconda.org/openeye/osx-arm64/openeye-toolkits-2026.1.0-py313_0.conda - pypi: ./ - pypi: https://files.pythonhosted.org/packages/08/52/eefeba09be4ef2a1eb989eb92934561e8e502a6ee3c32654996e4be7e399/types_pyyaml-6.0.12.20260815-py3-none-any.whl - pypi: https://files.pythonhosted.org/packages/12/11/263ced30149fbede00614a11f01de792842d3636b5a9e3abfee34e764a0e/scipy_stubs-1.18.1.0-py3-none-any.whl From 936dbe51c9c69e49d07d0f990f78b1490278c4bb Mon Sep 17 00:00:00 2001 From: "Matthew W. Thompson" Date: Mon, 24 Aug 2026 11:27:03 -0500 Subject: [PATCH 14/26] Kick --- .github/workflows/ci.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 826b324..f66c7b3 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -39,11 +39,11 @@ jobs: - name: Write OE license file if: ${{ !contains(env.PIXI_ENV, 'docs') }} - env: - SECRET_OE_LICENSE: ${{ secrets.OE_LICENSE }} run: | echo "${SECRET_OE_LICENSE}" > ${OE_LICENSE} pixi run -e "${{ env.PIXI_ENV }}" python -c "from openeye import oechem; assert oechem.OEChemIsLicensed()" + env: + SECRET_OE_LICENSE: ${{ secrets.OE_LICENSE }} - name: Run type-checker run: pixi run -e "${{ env.PIXI_ENV }}" type-check From 441d47a2a4cf227ff16771d9a522d551fcfcd46a Mon Sep 17 00:00:00 2001 From: "Matthew W. Thompson" Date: Mon, 24 Aug 2026 11:41:29 -0500 Subject: [PATCH 15/26] Safer skipping --- fitlib/_tests/potentials/test_nonbonded.py | 1 + fitlib/_tests/potentials/test_potentials.py | 1 + 2 files changed, 2 insertions(+) diff --git a/fitlib/_tests/potentials/test_nonbonded.py b/fitlib/_tests/potentials/test_nonbonded.py index 05feb2f..62516cf 100644 --- a/fitlib/_tests/potentials/test_nonbonded.py +++ b/fitlib/_tests/potentials/test_nonbonded.py @@ -1,6 +1,7 @@ import pytest pytest.importorskip("openmm") +pytest.importorskip("NNPOps") import copy import math diff --git a/fitlib/_tests/potentials/test_potentials.py b/fitlib/_tests/potentials/test_potentials.py index 904bd16..37b2da6 100644 --- a/fitlib/_tests/potentials/test_potentials.py +++ b/fitlib/_tests/potentials/test_potentials.py @@ -1,6 +1,7 @@ import pytest pytest.importorskip("openmm") +pytest.importorskip("NNPOps") import numpy import openff.interchange From c5cf94da81b36e70eb6e96b8bf4b7964d98c1de8 Mon Sep 17 00:00:00 2001 From: "Matthew W. Thompson" Date: Mon, 24 Aug 2026 11:54:04 -0500 Subject: [PATCH 16/26] Remove some debug --- .github/workflows/ci.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index f66c7b3..9905e91 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -18,7 +18,7 @@ jobs: PIXI_ENV: ${{ matrix.base }}-py${{ matrix.python-version }} strategy: - fail-fast: true + fail-fast: false matrix: os: [ubuntu-latest, macos-latest] python-version: ["312", "313"] From 92b4f950e1b23dac43fe64cbf207bcf87c760df7 Mon Sep 17 00:00:00 2001 From: "Matthew W. Thompson" Date: Mon, 24 Aug 2026 18:45:37 -0500 Subject: [PATCH 17/26] Bump --- pixi.lock | 140 +++++++++++++++++++----------------------------------- pixi.toml | 3 +- 2 files changed, 50 insertions(+), 93 deletions(-) diff --git a/pixi.lock b/pixi.lock index fd3343c..ba4c8da 100644 --- a/pixi.lock +++ b/pixi.lock @@ -349,8 +349,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/notebook-shim-0.2.4-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/openff-amber-ff-ports-2025.09.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/openff-forcefields-2026.01.0-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/openff-interchange-base-0.5.4-pyhac95a24_3.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/openff-toolkit-base-0.18.1-pyhd8ed1ab_3.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/openff-toolkit-base-0.19.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/openff-units-0.4.0-pyhac95a24_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/openff-utilities-0.1.17-pyhac95a24_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/overrides-7.7.0-pyhd8ed1ab_1.conda @@ -433,7 +432,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/werkzeug-3.1.8-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.48.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/widgetsnbextension-4.0.16-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/xmltodict-1.0.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/xmltodict-1.0.4-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/xyzservices-2026.3.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-4.1.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/openeye/linux-64/openeye-toolkits-2026.1.0-py312_0.conda @@ -563,8 +562,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/notebook-shim-0.2.4-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/openff-amber-ff-ports-2025.09.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/openff-forcefields-2026.01.0-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/openff-interchange-base-0.5.4-pyhac95a24_3.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/openff-toolkit-base-0.18.1-pyhd8ed1ab_3.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/openff-toolkit-base-0.19.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/openff-units-0.4.0-pyhac95a24_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/openff-utilities-0.1.17-pyhac95a24_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/overrides-7.7.0-pyhd8ed1ab_1.conda @@ -647,7 +645,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/werkzeug-3.1.8-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.48.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/widgetsnbextension-4.0.16-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/xmltodict-1.0.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/xmltodict-1.0.4-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/xyzservices-2026.3.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-4.1.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/_openmp_mutex-4.5-7_kmp_llvm.conda @@ -1213,8 +1211,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/notebook-shim-0.2.4-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/openff-amber-ff-ports-2025.09.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/openff-forcefields-2026.01.0-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/openff-interchange-base-0.5.4-pyhac95a24_3.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/openff-toolkit-base-0.18.1-pyhd8ed1ab_3.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/openff-toolkit-base-0.19.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/openff-units-0.4.0-pyhac95a24_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/openff-utilities-0.1.17-pyhac95a24_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/overrides-7.7.0-pyhd8ed1ab_1.conda @@ -1296,7 +1293,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/websocket-client-1.9.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/werkzeug-3.1.8-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/widgetsnbextension-4.0.16-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/xmltodict-1.0.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/xmltodict-1.0.4-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/xyzservices-2026.3.0-pyhd8ed1ab_0.conda - 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conda: https://conda.anaconda.org/conda-forge/osx-arm64/_openmp_mutex-4.5-7_kmp_llvm.conda @@ -15804,9 +15790,9 @@ packages: run_exports: {} size: 963493 timestamp: 1786638545421 -- conda: https://conda.anaconda.org/conda-forge/noarch/openff-toolkit-base-0.18.1-pyhd8ed1ab_1.conda - sha256: 6f2c4e68227c0f03cecbe1d0c535b9343938f08c216a4ce5518ebfa86de49c96 - md5: 5627410aaceb7ac39d5bdbd97b43218f +- conda: https://conda.anaconda.org/conda-forge/noarch/openff-toolkit-base-0.19.0-pyhd8ed1ab_0.conda + sha256: 6b1316d31654fc0d620349630376a75d68ae6d678f883ff3c9bb0c811f1522b6 + md5: a3fc791eef14a73b8ed22292628dcc2d depends: - bson - cachetools @@ -15817,49 +15803,21 @@ packages: - openff-units >=0.2 - openff-utilities - packaging - - python >=3.12 + - python >=3.12,<3.15.0a0 - python-constraint - - typing_extensions - - xmltodict <1.0.3 + - rdkit !=2024.03.6 + - xmltodict constrains: - openforcefield ==9999999999 - - qcportal >=0.50.0 - pydantic <3.0.0a0 - license: MIT - license_family: MIT - purls: - - pkg:pypi/openff-toolkit?source=hash-mapping - run_exports: {} - size: 7997663 - timestamp: 1781295781168 -- conda: https://conda.anaconda.org/conda-forge/noarch/openff-toolkit-base-0.18.1-pyhd8ed1ab_3.conda - sha256: 6443614a63d6360f09d484839070f57248562879b0195efbcc2c50a1cf168854 - md5: 201ebc6e389512565a8a046fddfc6ce0 - depends: - - bson - - cachetools - - networkx >=2.5 - - numpy - - openff-amber-ff-ports >=2025.09.0 - - openff-forcefields - - openff-units >=0.2 - - openff-utilities - - packaging - - python >=3.12,<3.14.0a0 - - python-constraint - - rdkit !=2024.03.6 - - xmltodict <1.0.3 - constrains: - qcportal >=0.50.0 - - pydantic <3.0.0a0 - - openforcefield ==9999999999 license: MIT license_family: MIT purls: - pkg:pypi/openff-toolkit?source=hash-mapping run_exports: {} - size: 8038029 - timestamp: 1784238291644 + size: 8003825 + timestamp: 1786657086420 - conda: https://conda.anaconda.org/conda-forge/noarch/openff-units-0.4.0-pyhac95a24_1.conda sha256: e97c98923c85855386d583997a598d91c6c7445e5dac6b3af826341188395538 md5: dfe3a840e8372cab543ccbc80054e542 @@ -17426,9 +17384,9 @@ packages: run_exports: {} size: 901988 timestamp: 1787044664327 -- conda: https://conda.anaconda.org/conda-forge/noarch/xmltodict-1.0.2-pyhcf101f3_0.conda - sha256: 5001a8b0a26810f5b8e20a05bca8cd458b0f6dcfbe00bf648aec017b1fd341f1 - md5: e91301ac8b2ad640df6165db24914332 +- conda: https://conda.anaconda.org/conda-forge/noarch/xmltodict-1.0.4-pyhcf101f3_0.conda + sha256: 7588e77a5d3885145e693d8b98493952f6efac8f3fabb1c218cd0cbd1a739fad + md5: 0f02dbcae61967ced21fea829a8ee927 depends: - python >=3.10 - python @@ -17437,8 +17395,8 @@ packages: purls: - pkg:pypi/xmltodict?source=hash-mapping run_exports: {} - size: 20026 - timestamp: 1758191083015 + size: 20673 + timestamp: 1771770296472 - conda: https://conda.anaconda.org/conda-forge/noarch/xyzservices-2026.3.0-pyhd8ed1ab_0.conda sha256: 663ea9b00d68c2da309114923924686ab6d3f59ef1b196c5029ba16799e7bb07 md5: 4487b9c371d0161d54b5c7bbd890c0fc diff --git a/pixi.toml b/pixi.toml index 526be7c..1ed85ba 100644 --- a/pixi.toml +++ b/pixi.toml @@ -19,8 +19,7 @@ pip = "*" msgpack-python = "*" pydantic = "2.*" pydantic-units = "*" -openff-toolkit-base = "0.18.*" -openff-interchange-base = "0.5.*" +openff-toolkit-base = "0.19.*" pytorch = "*" pyarrow = "*" datasets = "*" From e8c1bb3f0b5f2eb8db095ec4fdb217624b22a5e2 Mon Sep 17 00:00:00 2001 From: "Matthew W. Thompson" Date: Tue, 1 Sep 2026 14:17:06 -0500 Subject: [PATCH 18/26] Update lockfile --- pixi.lock | 7022 +++++++++++++++++++++++++++-------------------------- 1 file changed, 3522 insertions(+), 3500 deletions(-) diff --git a/pixi.lock b/pixi.lock index acce4dd..445bbb1 100644 --- a/pixi.lock +++ b/pixi.lock @@ -1,5 +1,13 @@ version: 7 platforms: +- name: cuda12 + subdir: linux-64 + virtual-packages: + - __cuda=12.0 + - __unix=0=0 + - __linux=4.18 + - __glibc=2.28 + - __archspec=0=x86_64 - name: linux-64 virtual-packages: - __unix=0=0 @@ -11,14 +19,6 @@ platforms: - __unix=0=0 - __osx=13.0 - __archspec=0=m1 -- name: p1 - subdir: linux-64 - virtual-packages: - - __cuda=12.0 - - __unix=0=0 - - __linux=4.18 - - __glibc=2.28 - - __archspec=0=x86_64 environments: base-py312: channels: @@ -27,11 +27,11 @@ environments: indexes: - https://pypi.org/simple packages: - linux-64: - - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-7_kmp_llvm.conda + cuda12: + - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-8_kmp_llvm.conda - 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"This example will show how the gradient of the potential energy with respect to force field parameters may be computed.\n", - "\n", - "We start be loading and parameterizing the molecule of interest." +This example will show how the gradient of the potential energy with respect to force field parameters may be computed. + +We start by loading and parameterizing the molecule of interest. ], "metadata": { "collapsed": false From 88d98eb8856f78031ce683e0c1670d52fac7cff3 Mon Sep 17 00:00:00 2001 From: "Matthew W. Thompson" Date: Thu, 3 Sep 2026 12:29:43 -0500 Subject: [PATCH 22/26] Changes from review --- examples/README.md | 1 - fitlib/_tests/potentials/test_potentials.py | 6 +- pixi.lock | 4456 +++++++++++++++---- pixi.toml | 42 +- 4 files changed, 3698 insertions(+), 807 deletions(-) diff --git a/examples/README.md b/examples/README.md index 83377f5..2bee26f 100644 --- a/examples/README.md +++ b/examples/README.md @@ -5,5 +5,4 @@ This directory contains a number of examples of how to use `fitlib`. They curren * [Evaluating the energy of a water dimer with virtual sites](compute-energy.ipynb) * [Minimizing the conformer of a molecule](conformer-minimization.ipynb) * [Computing the gradient of the energy w.r.t. force field parameters](parameter-gradients.ipynb) -* [Registering custom parameter handlers](custom-handler.ipynb) * [Differentiably compute ensemble averages from MD simulations](md-simulations.ipynb) diff --git a/fitlib/_tests/potentials/test_potentials.py b/fitlib/_tests/potentials/test_potentials.py index 37b2da6..edb5b5c 100644 --- a/fitlib/_tests/potentials/test_potentials.py +++ b/fitlib/_tests/potentials/test_potentials.py @@ -3,6 +3,8 @@ pytest.importorskip("openmm") pytest.importorskip("NNPOps") +from fittlib.mm import generate_system_coords + import numpy import openff.interchange import openff.interchange.models @@ -244,12 +246,10 @@ def test_compute_energy_v_sites(): @pytest.mark.parametrize("periodic", [True, False]) def test_energy_backward_pass(periodic): - 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name: lxml - version: 6.1.2 - sha256: 7d506bdba580ecb1a6ad2e2b5c49445e66d3e1f95894885739094393a1aad237 - requires_dist: - - cssselect>=0.7 ; extra == 'cssselect' - - html5lib ; extra == 'html5' - - beautifulsoup4 ; extra == 'htmlsoup' - - lxml-html-clean ; extra == 'html-clean' - requires_python: '>=3.8' - pypi: https://files.pythonhosted.org/packages/f1/d9/7fb5aa316bc299258e68c73ba3bddbc499654a07f151cba08f6153988714/pathspec-1.1.1-py3-none-any.whl name: pathspec version: 1.1.1 diff --git a/pixi.toml b/pixi.toml index f4c5519..526edfb 100644 --- a/pixi.toml +++ b/pixi.toml @@ -57,8 +57,10 @@ openeye-toolkits = "*" [feature.ambertools.dependencies] ambertools = "*" -[feature.examples.dependencies] +[feature.packmol.dependencies] packmol = "*" + +[feature.examples.dependencies] jupyter = "*" nbconvert = "*" nglview = "*" @@ -121,7 +123,41 @@ base-py312 = ["py312", "openeye", "ambertools", "test", "typing"] base-py313 = ["py313", "openeye", "ambertools", "test", "typing"] mm-py312 = ["py312", "openeye", "ambertools", "mm", "test", "typing"] mm-py313 = ["py313", "openeye", "ambertools", "mm", "test", "typing"] -examples-py312 = ["py312", "mm", "openeye", "examples", "test", "typing"] -examples-py313 = ["py313", "mm", "openeye", "examples", "test", "typing"] +examples-py312 = [ + "py312", + "mm", + "openeye", + "packmol", + "examples", + "test", + "typing", +] +examples-py313 = [ + "py313", + "mm", + "openeye", + "packmol", + "examples", + "test", + "typing", +] +examples-ambertools-py312 = [ + "py312", + "mm", + "ambertools", + "openeye", + "examples", + "test", + "typing", +] +examples-ambertools-py313 = [ + "py313", + "mm", + "ambertools", + "openeye", + "examples", + "test", + "typing", +] dev = ["py312", "mm", "openeye", "test", "typing", "dev"] docs = ["py312", "docs"] From 4705f9fe3a7fe02966f38d90f4494fb6bd63b74a Mon Sep 17 00:00:00 2001 From: "Matthew W. Thompson" Date: Thu, 3 Sep 2026 13:37:12 -0500 Subject: [PATCH 23/26] Fix --- fitlib/_tests/potentials/test_potentials.py | 2 +- pixi.lock | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/fitlib/_tests/potentials/test_potentials.py b/fitlib/_tests/potentials/test_potentials.py index edb5b5c..3f21665 100644 --- a/fitlib/_tests/potentials/test_potentials.py +++ b/fitlib/_tests/potentials/test_potentials.py @@ -3,7 +3,7 @@ pytest.importorskip("openmm") pytest.importorskip("NNPOps") -from fittlib.mm import generate_system_coords +from fitlib.mm import generate_system_coords import numpy import openff.interchange diff --git a/pixi.lock b/pixi.lock index ed5e258..d61a9c1 100644 --- a/pixi.lock +++ b/pixi.lock @@ -21164,7 +21164,7 @@ packages: license: MIT license_family: MIT purls: - - pkg:pypi/rpds-py?source=compressed-mapping + - pkg:pypi/rpds-py?source=hash-mapping run_exports: {} size: 300007 timestamp: 1788228320942 From 9667e743ec1c183fe8b56307b545b686191a1e89 Mon Sep 17 00:00:00 2001 From: "Matthew W. Thompson" Date: Thu, 3 Sep 2026 14:21:30 -0500 Subject: [PATCH 24/26] Revert "Fix formatting in parameter gradients example" This reverts commit 45fbc3f7ad9033d57c56601316bae7e6e5c9f48e. --- examples/parameter-gradients.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/parameter-gradients.ipynb b/examples/parameter-gradients.ipynb index 2ea1667..54babc6 100644 --- a/examples/parameter-gradients.ipynb +++ b/examples/parameter-gradients.ipynb @@ -5,9 +5,9 @@ "source": [ "# Parameter Gradients\n", "\n", -This example will show how the gradient of the potential energy with respect to force field parameters may be computed. - -We start by loading and parameterizing the molecule of interest. + "This example will show how the gradient of the potential energy with respect to force field parameters may be computed.\n", + "\n", + "We start be loading and parameterizing the molecule of interest." ], "metadata": { "collapsed": false From 43ec0a1ff9546030f31f0e1142edcb2fd1686828 Mon Sep 17 00:00:00 2001 From: "Matthew W. Thompson" Date: Thu, 3 Sep 2026 14:23:41 -0500 Subject: [PATCH 25/26] Fix --- examples/parameter-gradients.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/parameter-gradients.ipynb b/examples/parameter-gradients.ipynb index 54babc6..3587d52 100644 --- a/examples/parameter-gradients.ipynb +++ b/examples/parameter-gradients.ipynb @@ -7,7 +7,7 @@ "\n", "This example will show how the gradient of the potential energy with respect to force field parameters may be computed.\n", "\n", - "We start be loading and parameterizing the molecule of interest." + "We start by loading and parameterizing the molecule of interest." ], "metadata": { "collapsed": false From f902f0a167404ba5fb694765ba49d09e3d653cd8 Mon Sep 17 00:00:00 2001 From: "Matthew W. Thompson" Date: Thu, 3 Sep 2026 14:28:55 -0500 Subject: [PATCH 26/26] More fixes --- fitlib/_tests/potentials/test_potentials.py | 3 +-- pixi.toml | 2 +- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/fitlib/_tests/potentials/test_potentials.py b/fitlib/_tests/potentials/test_potentials.py index 3f21665..9b279e4 100644 --- a/fitlib/_tests/potentials/test_potentials.py +++ b/fitlib/_tests/potentials/test_potentials.py @@ -3,8 +3,6 @@ pytest.importorskip("openmm") pytest.importorskip("NNPOps") -from fitlib.mm import generate_system_coords - import numpy import openff.interchange import openff.interchange.models @@ -17,6 +15,7 @@ import fitlib._tests.utils import fitlib.converters import fitlib.utils +from fitlib.mm import generate_system_coords from fitlib.potentials import ( broadcast_exceptions, broadcast_parameters, diff --git a/pixi.toml b/pixi.toml index c718a62..d815a06 100644 --- a/pixi.toml +++ b/pixi.toml @@ -10,7 +10,7 @@ platforms = [ [tasks] test = "python -m pytest -n auto --cov=fitlib --cov-append --cov-report=xml --color=yes fitlib/_tests/" -# doesn't include examples/md-simulations.ipynb, should it? +# doesn't include examples/md-simulations.ipynb because it's not great without CUDA test-examples = "mkdir -p .nbconvert && jupyter nbconvert --to notebook --execute --output-dir .nbconvert examples/compute-energy.ipynb examples/conformer-minimization.ipynb examples/parameter-gradients.ipynb" type-check = "python -m mypy fitlib/" make_docs = "sphinx-build -b html docs docs/_build/html"