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Do you want to train a force field?
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| name: build-a-force-field | |
| channels: | |
| - conda-forge | |
| - psi4/label/dev | |
| - defaults | |
| dependencies: | |
| - python | |
| - pip | |
| # Common dependencies / utilities | |
| - openff-toolkit | |
| - openff-qcsubmit | |
| - rdkit | |
| - tqdm | |
| - jupyter | |
| # Local QC data generation | |
| - qcfractal >=0.15.6 | |
| - torsiondrive | |
| - geometric | |
| - psi4 | |
| - basis_set_exchange | |
| # Remote QC data retrieval | |
| - qcportal | |
| # Force field optimizer | |
| - forcebalance | |
| - chemper | |
| - pip: | |
| - git+git://github.com/openforcefield/bespoke-fit.git@master |
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Do you want to train a force field?\n", | |
| "\n", | |
| "Within Open Force Field (as the name might suggest) a lot of time and effort is spent exploring exactly how should\n", | |
| "force fields be trained - what data should be fit to? how should that data influence the optimization e.g. L1, L2? what\n", | |
| "classical functional forms should be used for the force field interactions? or should everything just be machine learned\n", | |
| "*a la* ANI and friends.\n", | |
| "\n", | |
| "A big part of this process is automation. No one wants to manually trawl through millions of molecules by hand when\n", | |
| "building a train / test set, or painstakingly set up and store thousands of quantum chemical calculations to train\n", | |
| "against. Ideally, training a force field should be at the level of 'these are the kinds of chemistry's I'm interested\n", | |
| "in, this is the type of data I want to train against, let me vary these hyperparameters, and let me hit a big green GO\n", | |
| "button!'.\n", | |
| "\n", | |
| "It is this level of abstraction that Open Force Field is working towards, both in terms of its software pipelines and\n", | |
| "its general workflows and procedures, and it is these that I will be diving into as part of this blog post!\n", | |
| "\n", | |
| ":::note\n", | |
| " The Open Force Field infrastructure is currently rapidly evolving and so parts of this blog post may become\n", | |
| " out of date as time progresses.\n", | |
| ":::\n", | |
| "\n", | |
| "I will be writing this post as if it were to be written in a Jupyter notebook (indeed, I am writing this in a Jupyter\n", | |
| "notebook!) so that you the reader can follow along and in principle build your own force field!\n", | |
| "\n", | |
| "It will be structured into several parts:\n", | |
| "\n", | |
| "* first, we will be locally generating a small quantum chemical data set of torsion drives that we can train the\n", | |
| " parameters in our force field against.\n", | |
| "\n", | |
| "* next, we will complement the torsion drives with a set of optimised geometries that we will retrieve from the\n", | |
| " bountiful [QCArchive](https://qcarchive.molssi.org/) repository\n", | |
| "\n", | |
| "* after that, we will create a *schema* (essentially a blueprint) defining how the force field will be trained\n", | |
| "\n", | |
| "* and finally, we will hit the big green GO button and train the force field!\n", | |
| "\n", | |
| "#### Following along\n", | |
| "\n", | |
| "If you would like to follow along with the steps that I will be showing here I recommend (unless running in binder) first creating a new [conda\n", | |
| "environment](https://docs.conda.io/en/latest/miniconda.html#) with all of the neccessary dependencies:" | |
| ], | |
| "metadata": { | |
| "collapsed": false | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "outputs": [], | |
| "source": [ | |
| "#!conda env create --name build-a-force-field --file environment.yaml\n", | |
| "#!conda activate build-a-force-field" | |
| ], | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%%\n" | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "I will also be hiding most of the warnings that come out of the OpenFF toolkit to make this post easier to follow:" | |
| ], | |
| "metadata": { | |
| "collapsed": false | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "outputs": [], | |
| "source": [ | |
| "import warnings\n", | |
| "warnings.filterwarnings('ignore')\n", | |
| "import logging\n", | |
| "logging.getLogger(\"openff.toolkit\").setLevel(logging.ERROR)" | |
| ], | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%%\n" | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "*this is **not** recommended in general though!*\n", | |
| "\n", | |
| "## Generating a QC torsion drive training set\n", | |
| "\n", | |
| "As you might expect, the first step in training a new force field is generating a suite of reference data to train\n", | |
| "against. While the Open Force Field consortium routinely works with both QC and [experimental physical property\n", | |
| "data](https://openff-evaluator.readthedocs.io/en/latest/datasets/curation.html) when building its force fields,\n", | |
| "I will 'only' be talking about training to QC data here to keep things short and sweet!\n", | |
| "\n", | |
| "The two main players in Open Force Fields QC data generation are the MolSSI QC ecosystem ([QCFractal](\n", | |
| "http://docs.qcarchive.molssi.org/projects/qcfractal/en/latest/) in particular) and the [OpenFF QCSubmit](\n", | |
| "https://openff-qcsubmit.readthedocs.io/en/latest/) package.\n", | |
| "\n", | |
| "So what do these two packages do? Well, to quote their documentation, QCFractal is\n", | |
| "\n", | |
| "> a distributed compute and database platform for quantum chemistry.\n", | |
| "\n", | |
| "and QCSubmit provides\n", | |
| "\n", | |
| "> automated tools for submitting molecules to QCFractal and retrieving the results\n", | |
| "\n", | |
| "Simply put, QCSubmit allows us to curate large datasets of molecules that QCFractal can then perform QC calculations on\n", | |
| "in a way that scales across hundreds of compute resources without human intervention - i.e. no more manually runing\n", | |
| "Psi4 or Gaussian :tada:!\n", | |
| "\n", | |
| "#### Preparing the training set\n", | |
| "\n", | |
| "So how we do use these packages? Let's start by specifying a bunch of molecules that we want to train our force field\n", | |
| "against!\n", | |
| "\n", | |
| "While the consortium is currently working on procedures to fully automate the process of [selecting training sets](\n", | |
| "https://www.youtube.com/watch?v=HBpzVZI2rkI&ab_channel=OpenForceFieldInitiative) to ensure comprehensive swathes of\n", | |
| "relevant chemical spaces are covered, it does not yet provide packaged tools to do this (watch this space!).\n", | |
| "\n", | |
| "Instead, for this blog post we will be training a force field for (wait for it) some linear alkanes :confetti_ball:!\n", | |
| "Ok it may not be the most exciting set, but I mainly want to show the process by which we can train a force field\n", | |
| "without needing a whole supercomputers worth of resources. This process itself can (and routinely does) handle datasets\n", | |
| "of thousands of unique molecules with diverse chemistry's.\n", | |
| "\n", | |
| "Let's define these molecules now:" | |
| ], | |
| "metadata": { | |
| "collapsed": false | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "outputs": [], | |
| "source": [ | |
| "from openff.toolkit.topology import Molecule\n", | |
| "\n", | |
| "training_smiles = [\n", | |
| " \"CC\",\n", | |
| " \"CCCC\",\n", | |
| "]\n", | |
| "\n", | |
| "training_molecules = [Molecule.from_smiles(smiles) for smiles in training_smiles]" | |
| ], | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%%\n" | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "We have the molecules, but how do we turn these into a set of QC calculations to run? This is where QCSubmit comes in!\n", | |
| "The package allows us to create a 'factory' that does exactly this mapping.\n", | |
| "\n", | |
| "Let's create the factory we'll be using:" | |
| ], | |
| "metadata": { | |
| "collapsed": false | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": "TorsiondriveDatasetFactory(qc_specifications={'default': QCSpec(method='B3LYP-D3BJ', basis='DZVP', program='psi4', spec_name='default', spec_description='Standard OpenFF optimization quantum chemistry specification.', store_wavefunction=<WavefunctionProtocolEnum.none: 'none'>, implicit_solvent=None, keywords=None)}, maxiter=200, driver=<DriverEnum.gradient: 'gradient'>, scf_properties=[<SCFProperties.Dipole: 'dipole'>, <SCFProperties.Quadrupole: 'quadrupole'>, <SCFProperties.WibergLowdinIndices: 'wiberg_lowdin_indices'>, <SCFProperties.MayerIndices: 'mayer_indices'>], priority='normal', dataset_tags=['openff'], compute_tag='openff', type='TorsiondriveDatasetFactory', workflow=[ScanEnumerator(type='ScanEnumerator', torsion_scans=[Scan1D(smarts1='[*:1]-[#6:2]-[#6:3]-[*:4]', scan_range1=(-150, 180), scan_increment=[30])], double_torsion_scans=[], improper_scans=[]), StandardConformerGenerator(type='StandardConformerGenerator', toolkit='rdkit', rms_cutoff=None, max_conformers=10, clear_existing=True)], optimization_program=GeometricProcedure(program='geometric', coordsys='dlc', enforce=0.1, epsilon=0.0, reset=True, qccnv=True, molcnv=False, check=0, trust=0.1, tmax=0.3, maxiter=300, convergence_set='GAU', constraints={}), grid_spacing=[15], energy_upper_limit=0.05, dihedral_ranges=None, energy_decrease_thresh=None)" | |
| }, | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "from openff.qcsubmit.common_structures import QCSpec\n", | |
| "from openff.qcsubmit.factories import TorsiondriveDatasetFactory\n", | |
| "from openff.qcsubmit.workflow_components import (\n", | |
| " Scan1D, ScanEnumerator, StandardConformerGenerator\n", | |
| ")\n", | |
| "\n", | |
| "torsion_drive_factory = TorsiondriveDatasetFactory(\n", | |
| " qc_specifications={\n", | |
| " \"default\": QCSpec(\n", | |
| " method=\"B3LYP-D3BJ\", basis=\"DZVP\", program=\"psi4\"\n", | |
| " ),\n", | |
| " },\n", | |
| " workflow=[\n", | |
| " ScanEnumerator(\n", | |
| " torsion_scans=[\n", | |
| " Scan1D(\n", | |
| " smarts1=\"[*:1]-[#6:2]-[#6:3]-[*:4]\",\n", | |
| " scan_range1=(-150, 180),\n", | |
| " scan_increment=[30]\n", | |
| " )\n", | |
| " ]\n", | |
| " ),\n", | |
| " StandardConformerGenerator(toolkit=\"rdkit\", max_conformers=10)\n", | |
| " ],\n", | |
| ")\n", | |
| "\n", | |
| "torsion_drive_factory" | |
| ], | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%%\n" | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "As you can probably see we have here created a `TorsiondriveDatasetFactory` that will be used to produce, as you can\n", | |
| "probably guess, datasets of QC torsion drives. You probably also won't be surprised to learn that QCSubmit provides\n", | |
| "[several types of factories](https://openff-qcsubmit.readthedocs.io/en/latest/api.html) for the different types of QC\n", | |
| "calculation supported by QCFractal. For now though, let's focus on torsion drives.\n", | |
| "\n", | |
| "We provide 2 key ingredients to our factory:\n", | |
| "\n", | |
| "* a set of QC 'specifications' - a QC specification is where we define the level of theory as well as the program with\n", | |
| " which we want to run our calculations. Here we have used the 'Open Force Field defaults' which were carefully chosen\n", | |
| " to provide a balance of speed and accuracy at reproducing conformational energies.\n", | |
| "\n", | |
| "* a 'workflow' - the workflow contains a set of processing steps that will be applied to an input set of molecules.\n", | |
| " These range from generating a set of conformers for each molecule to start the torsion drive from, enumerating all\n", | |
| " tautomers and protomers of the input molecules, fragmenting the input molecules to speed up the QC calculations and\n", | |
| " much more!\n", | |
| "\n", | |
| "Here we have used two workflow components: i) a conformer generator which ensures that [we have several conformers to\n", | |
| "start the torsion drive from](https://torsiondrive.readthedocs.io/en/latest/) and ii) a component that defines exactly\n", | |
| "which torsions should be scanned. As with most things in the OpenFF ecosystem, we have used a [SMARTS pattern](\n", | |
| "https://docs.eyesopen.com/toolkits/cpp/oechemtk/SMARTS.html) to define the chemical environments that we would like to\n", | |
| "scan. This particular pattern will match all unique torsions around a C-C single bond.\n", | |
| "\n", | |
| "Great - we have our molecules and we have a factory. Let's now combine the two and create the dataset of QC calculations\n", | |
| "that QCFractal should run:" | |
| ], | |
| "metadata": { | |
| "collapsed": false | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "outputs": [ | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "Deduplication : 100%|████████████| 2/2 [00:00<00:00, 504.43it/s]\n", | |
| "ScanEnumerator : 0%| | 0/2 [00:00<?, ?it/s]Warning: Unable to load toolkit 'AmberTools'. \n", | |
| "Warning: Unable to load toolkit 'AmberTools'. \n", | |
| "ScanEnumerator : 100%|█████████████| 2/2 [00:01<00:00, 1.07it/s]\n", | |
| "StandardConformerGenerator : 0%| | 0/2 [00:00<?, ?it/s]Warning: Unable to load toolkit 'AmberTools'. \n", | |
| "Warning: Unable to load toolkit 'AmberTools'. \n", | |
| "StandardConformerGenerator : 100%|█████████████| 2/2 [00:04<00:00, 2.12s/it]\n", | |
| "Preparation : 100%|█████████████| 2/2 [00:00<00:00, 47.51it/s]\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "text/plain": "3" | |
| }, | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "torsion_drive_dataset = torsion_drive_factory.create_dataset(\n", | |
| " molecules=training_molecules,\n", | |
| " dataset_name=\"Alkane Torsion Drives\",\n", | |
| " description=\"A very basic dataset containing three linear alkanes: \"\n", | |
| " \"ethane, propane, and butane.\",\n", | |
| " tagline=\"A very basic dataset containing three linear alkanes.\",\n", | |
| ")\n", | |
| "torsion_drive_dataset.n_records" | |
| ], | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%%\n" | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "#### Computing the training set\n", | |
| "\n", | |
| "With the QC dataset prepared, we can move on to actually running the calculations that will be used in training.\n", | |
| "\n", | |
| "The workhorse of the Open Force Field QC data generation is the QCFractal package and its [surrounding ecosystem](\n", | |
| "http://docs.qcarchive.molssi.org/en/latest/). It provides a simple interface for computing and storing data sets of QC\n", | |
| "calculations, being able to scale from a single laptop all the way up to multiple supercomputers located around the\n", | |
| "globe.\n", | |
| "\n", | |
| "While the bulk of the QC training and test sets the Open Force Field uses (as we will see later in this post) are\n", | |
| "computed and stored in MolSSIs public QCFractal instance ([the QCArchive](https://qcarchive.molssi.org/)), here we will\n", | |
| "be spinning up a temporary local QCFractal to better showcase how simple generating new QC data is:" | |
| ], | |
| "metadata": { | |
| "collapsed": false | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "outputs": [], | |
| "source": [ | |
| "from qcfractal import FractalSnowflakeHandler\n", | |
| "from qcportal.client import FractalClient\n", | |
| "\n", | |
| "local_fractal_instance = FractalSnowflakeHandler(ncores=16)\n", | |
| "local_fractal_client = FractalClient(local_fractal_instance)" | |
| ], | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%%\n" | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "*see the [main QCFractal documentation](http://docs.qcarchive.molssi.org/projects/qcfractal/en/latest/setup_quickstart.html)\n", | |
| "for more information about setting up local instances to compute and store your non-public data*\n", | |
| "\n", | |
| "Submitting, computing, and finally storing a QC dataset is as easy as calling the submit command and providing the\n", | |
| "address of the server that should handle the submission:" | |
| ], | |
| "metadata": { | |
| "collapsed": false | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "outputs": [], | |
| "source": [ | |
| "submission = torsion_drive_dataset.submit(local_fractal_client)" | |
| ], | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%%\n" | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "We can check on the progress of our calculations by querying the fractal instance:" | |
| ], | |
| "metadata": { | |
| "collapsed": false | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": "[TorsionDriveRecord(id='1', status='INCOMPLETE'),\n TorsionDriveRecord(id='2', status='INCOMPLETE'),\n TorsionDriveRecord(id='3', status='INCOMPLETE')]" | |
| }, | |
| "execution_count": 7, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "local_fractal_client.query_procedures(procedure=\"torsiondrive\", status=None)" | |
| ], | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%%\n" | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Let's query the server every 60 seconds until we get the all clear that our calculations have finished:" | |
| ], | |
| "metadata": { | |
| "collapsed": false | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "N COMPLETED: 3 N ERRORED: 0 ELAPSED=2824.879119157791s\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "from time import sleep, time\n", | |
| "\n", | |
| "n_completed = 0\n", | |
| "n_errored = 0\n", | |
| "\n", | |
| "start_time = time()\n", | |
| "\n", | |
| "while n_completed + n_errored != torsion_drive_dataset.n_records:\n", | |
| "\n", | |
| " sleep(60)\n", | |
| "\n", | |
| " n_completed = len(local_fractal_client.query_procedures(\n", | |
| " procedure=\"torsiondrive\", status=\"COMPLETE\"\n", | |
| " ))\n", | |
| " n_errored = len(local_fractal_client.query_procedures(\n", | |
| " procedure=\"torsiondrive\", status=\"ERROR\"\n", | |
| " ))\n", | |
| "\n", | |
| "print(f\"N COMPLETED: {n_completed} N ERRORED: {n_errored} ELAPSED={time() - start_time}s\")" | |
| ], | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%%\n" | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "If everything went well we should see that we have three completed torsion drives and zero errored.\n", | |
| "\n", | |
| "And that's it - we now have a set of torsion drives ready to train our force field against!\n", | |
| "\n", | |
| "## Retrieving a remote QC optimization training set\n", | |
| "\n", | |
| "Almost all the QC data that Open Force Field generates, whether that be for production force fields or smaller\n", | |
| "scientific studies, are made available via the public [QCArchive](https://qcarchive.molssi.org/) repository. This\n", | |
| "is not only part of the consortiums' effort to make all the science it performs completely accessible and reproducible,\n", | |
| "but so that anyone can use it as part of their efforts!\n", | |
| "\n", | |
| "We are going to retrieve a set of optimised geometries for our training molecules that were computed as part of a\n", | |
| "previous Open Force Field dataset. To do this we will again be using the fantastic QCSubmit package.\n", | |
| "\n", | |
| "The QCSubmit package, in addition to data curation and submission, contains a suite of utilities for retrieving\n", | |
| "data from a running QCFractal instance. This functionality is provided by several ['result collections'](\n", | |
| "https://openff-qcsubmit.readthedocs.io/en/latest/examples/retrieving-results.html) - one for each type of QC data that\n", | |
| "can be retrieved.\n", | |
| "\n", | |
| "To pull down optimization data we will (surprise, surprise) using the optimization result collection:" | |
| ], | |
| "metadata": { | |
| "collapsed": false | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "outputs": [], | |
| "source": [ | |
| "# Create a client which allows us to connect to the main QCArchive server.\n", | |
| "qcarchive_client = FractalClient()\n", | |
| "\n", | |
| "# Retrieve the data set containing the molecules of interest.\n", | |
| "from openff.qcsubmit.results import OptimizationResultCollection\n", | |
| "\n", | |
| "optimization_result_collection = OptimizationResultCollection.from_server(\n", | |
| " client=qcarchive_client,\n", | |
| " datasets=[\"OpenFF Sandbox CHO PhAlkEthOH v1.0\"],\n", | |
| " spec_name=\"default\"\n", | |
| ")" | |
| ], | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%%\n" | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Here we have retrieved the 'OpenFF Sandbox CHO PhAlkEthOH v1.0' dataset which, as the name may suggest, contains\n", | |
| "a collection of optimised geometries of alkyl, aryl, and hydroxyl containing molecules composed only of carbon, oxygen\n", | |
| "and hydrogen.\n", | |
| "\n", | |
| "We can see that it contains thousands of unique molecules and corresponding optimized geometries:" | |
| ], | |
| "metadata": { | |
| "collapsed": false | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "N RESULTS: 12271\n", | |
| "N MOLECULES: 10301\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(f\"N RESULTS: {optimization_result_collection.n_results}\")\n", | |
| "print(f\"N MOLECULES: {optimization_result_collection.n_molecules}\")" | |
| ], | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%%\n" | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Ideally we'd like to filter down this collection to only include our molecules of interest... luckily, QCSubmit provides\n", | |
| "exactly this functionality:" | |
| ], | |
| "metadata": { | |
| "collapsed": false | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "N RESULTS: 2\n", | |
| "N MOLECULES: 2\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "from openff.qcsubmit.results.filters import SMILESFilter\n", | |
| "\n", | |
| "optimization_result_collection = optimization_result_collection.filter(\n", | |
| " SMILESFilter(smiles_to_include=training_smiles)\n", | |
| ")\n", | |
| "\n", | |
| "print(f\"N RESULTS: {optimization_result_collection.n_results}\")\n", | |
| "print(f\"N MOLECULES: {optimization_result_collection.n_molecules}\")" | |
| ], | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%%\n" | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Much better! The filters provided by QCSubmit go well beyond simple SMILES based filtering - I'd highly recommend taking\n", | |
| "a look at the [QCSubmit documentation](https://openff-qcsubmit.readthedocs.io/en/latest/examples/retrieving-results.html#Filtering-result-collections)\n", | |
| "for more details!\n", | |
| "\n", | |
| "To finish off, we will also retrieve the torsion drive results that we generated earlier on and store a reference to\n", | |
| "them in a result collection:" | |
| ], | |
| "metadata": { | |
| "collapsed": false | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "N RESULTS: 3\n", | |
| "N MOLECULES: 2\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "from openff.qcsubmit.results import TorsionDriveResultCollection\n", | |
| "\n", | |
| "torsion_drive_result_collection = TorsionDriveResultCollection.from_server(\n", | |
| " client=local_fractal_client,\n", | |
| " datasets=torsion_drive_dataset.dataset_name,\n", | |
| " spec_name=\"default\"\n", | |
| ")\n", | |
| "\n", | |
| "print(f\"N RESULTS: {torsion_drive_result_collection.n_results}\")\n", | |
| "print(f\"N MOLECULES: {torsion_drive_result_collection.n_molecules}\")" | |
| ], | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%%\n" | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Defining the training procedure\n", | |
| "\n", | |
| "We now have all the data that we will be training against. It's time to begin preparing the actual training process\n", | |
| "itself.\n", | |
| "\n", | |
| "In particular, we need to:\n", | |
| "\n", | |
| "* load the starting force field that we will be training\n", | |
| "* specify **which** parameters within the force field should be trained\n", | |
| "* specify the hyper-parameters that control **how** the parameters will be trained\n", | |
| "* define the 'targets' that we are training the parameters against\n", | |
| "\n", | |
| "All of this is made easy by the [OpenFF Bespoke](https://github.com/openforcefield/bespoke-fit) package. The OpenFF\n", | |
| "Bespoke package not only provides the functionality for creating tailor made force fields for individual molecules of\n", | |
| "interest, but also provides all the tools needed to train a general transferable force field.\n", | |
| "\n", | |
| ":::warning\n", | |
| " The OpenFF Bespoke package is still pre-release and is rapidly being developed. While every effort has been made to\n", | |
| " ensure scientific correctness, bugs do happen, and we advise caution when using this package.\n", | |
| ":::\n", | |
| "\n", | |
| "#### Defining the parameters to train\n", | |
| "\n", | |
| "Starting from the top then, we need to load in the force field that we will be training. Here we are going to retrain a\n", | |
| "subset of the parameters from the OpenFF 1.3.0 force field:" | |
| ], | |
| "metadata": { | |
| "collapsed": false | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 13, | |
| "outputs": [], | |
| "source": [ | |
| "from openff.toolkit.typing.engines.smirnoff import ForceField\n", | |
| "\n", | |
| "initial_force_field_path = \"openff_unconstrained-1.3.0.offxml\"\n", | |
| "initial_force_field = ForceField(initial_force_field_path)" | |
| ], | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%%\n" | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "The force field contains hundreds of parameters that cover a huge span of chemical space. From these, we can easily\n", | |
| "extract those parameters that are relevant to our training set by generating a parameter 'coverage' report:" | |
| ], | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%% md\n" | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "BONDS\n", | |
| "['[#6X4:1]-[#6X4:2]', '[#6X4:1]-[#1:2]']\n", | |
| "ANGLES\n", | |
| "['[*:1]~[#6X4:2]-[*:3]', '[#1:1]-[#6X4:2]-[#1:3]']\n", | |
| "PROPER TORSIONS\n", | |
| "['[#6X4:1]-[#6X4:2]-[#6X4:3]-[#6X4:4]', '[#1:1]-[#6X4:2]-[#6X4:3]-[#6X4:4]', '[#1:1]-[#6X4:2]-[#6X4:3]-[#1:4]']\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "coverage = optimization_result_collection.smirnoff_coverage(initial_force_field)\n", | |
| "\n", | |
| "print(\"BONDS\")\n", | |
| "print([smirks for smirks in coverage[\"Bonds\"]])\n", | |
| "print(\"ANGLES\")\n", | |
| "print([smirks for smirks in coverage[\"Angles\"]])\n", | |
| "print(\"PROPER TORSIONS\")\n", | |
| "print([smirks for smirks in coverage[\"ProperTorsions\"]])" | |
| ], | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%%\n" | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "As can be seen there are two bond, two angle and three torsion parameters. If you aren't familiar with [SMIRKS](\n", | |
| "https://www.daylight.com/dayhtml/doc/theory/theory.smirks.html) patterns then the above may look a bit daunting...\n", | |
| "Quite simply, a SMIRKS pattern encodes a particular chemical environment. The OpenFF force fields, or more precisely,\n", | |
| "[SMIRNOFF](https://openforcefield.github.io/standards/standards/smirnoff/) force fields, use these patterns to define\n", | |
| "which parameters should be applied to which chemistries.\n", | |
| "\n", | |
| "As an example let's take a look at the SMIRKS parameter associated with our first bond parameter: `'[#6X4:1]-[#6X4:2]'`.\n", | |
| "In a more human language, this pattern defines a chemical environment composed of two sp3 carbon atoms that are joined\n", | |
| "by single bond.\n", | |
| "\n", | |
| "Here we are going to train to all the parameters identified by the coverage report:" | |
| ], | |
| "metadata": { | |
| "collapsed": false | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 15, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": "[BondSmirks(type='Bonds', smirks='[#6X4:1]-[#6X4:2]', attributes={'k', 'length'}),\n BondSmirks(type='Bonds', smirks='[#6X4:1]-[#1:2]', attributes={'k', 'length'}),\n AngleSmirks(type='Angles', smirks='[*:1]~[#6X4:2]-[*:3]', attributes={'k', 'angle'}),\n AngleSmirks(type='Angles', smirks='[#1:1]-[#6X4:2]-[#1:3]', attributes={'k', 'angle'}),\n ProperTorsionSmirks(type='ProperTorsions', smirks='[#6X4:1]-[#6X4:2]-[#6X4:3]-[#6X4:4]', attributes={'k1'}),\n ProperTorsionSmirks(type='ProperTorsions', smirks='[#1:1]-[#6X4:2]-[#6X4:3]-[#6X4:4]', attributes={'k1'}),\n ProperTorsionSmirks(type='ProperTorsions', smirks='[#1:1]-[#6X4:2]-[#6X4:3]-[#1:4]', attributes={'k1'})]" | |
| }, | |
| "execution_count": 15, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "from openff.bespokefit.schema.smirks import AngleSmirks, BondSmirks, ProperTorsionSmirks\n", | |
| "\n", | |
| "target_parameters = [\n", | |
| " *[\n", | |
| " BondSmirks(smirks=smirks, attributes={\"k\", \"length\"})\n", | |
| " for smirks in coverage[\"Bonds\"]\n", | |
| " ],\n", | |
| " *[\n", | |
| " AngleSmirks(smirks=smirks, attributes={\"k\", \"angle\"})\n", | |
| " for smirks in coverage[\"Angles\"]\n", | |
| " ],\n", | |
| " *[\n", | |
| " ProperTorsionSmirks(smirks=smirks, attributes={\"k1\"})\n", | |
| " for smirks in coverage[\"ProperTorsions\"]\n", | |
| " ],\n", | |
| "]\n", | |
| "target_parameters" | |
| ], | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%%\n" | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "We must specify both the SMIRKS pattern associated with the parameters that we want to train, as well as the\n", | |
| "individual attributes (e.g. the force constant `k`, the bond `length`, the torsion barrier height `k1`)\n", | |
| "\n", | |
| "For each type of parameter (e.g. bond, angle, proper torsion, etc.) that we will be training we must also specify the\n", | |
| "settings that will control how that parameter will be trained. Currently, the only hyper-parameter is a 'prior' to place\n", | |
| "on each type of parameter.\n", | |
| "\n", | |
| "Borrowing the language of Bayesian statistics, a 'prior' in this context refers to a light restraint placed on each\n", | |
| "type of parameter that ensures that parameter does not change too much from its initial (usually physically motivated)\n", | |
| "value. For more details I would recommend watching [this fantastic talk](https://youtu.be/F_CKbbhiaWQ?t=1070) by\n", | |
| "Lee-Ping Wang who created the [ForceBalance](https://github.com/leeping/forcebalance) tool that drives most of the\n", | |
| "parameter training undertaken by Open Force Field." | |
| ], | |
| "metadata": { | |
| "collapsed": false | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 16, | |
| "outputs": [], | |
| "source": [ | |
| "from openff.bespokefit.schema.smirnoff import (\n", | |
| " AngleAngleSettings,\n", | |
| " AngleForceSettings,\n", | |
| " BondForceSettings,\n", | |
| " BondLengthSettings,\n", | |
| " ProperTorsionSettings,\n", | |
| ")\n", | |
| "\n", | |
| "parameter_settings=[\n", | |
| " BondForceSettings(prior=1.0e02),\n", | |
| " BondLengthSettings(prior=1.0e-01),\n", | |
| " AngleForceSettings(prior=1.0e02),\n", | |
| " AngleAngleSettings(prior=2.0e01),\n", | |
| " ProperTorsionSettings(target=\"k\", prior=1.0),\n", | |
| "]" | |
| ], | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%%\n" | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "#### Defining the fitting 'targets'\n", | |
| "\n", | |
| "With the parameters that will be trained defined, we now need to define the 'targets' that we will be training them\n", | |
| "against.\n", | |
| "\n", | |
| "A fitting 'target' defines how deviations from the reference data we defined earlier should contribute to our\n", | |
| "loss ( / objective) function. Here we will be using two targets that are routinely used when training new Open Force\n", | |
| "Field force fields:\n", | |
| "\n", | |
| "##### OptGeo\n", | |
| "\n", | |
| "The OptGeo (short for optimised geometry) fitting target penalizes force field parameters that cause conformers that\n", | |
| "have been minimised using the force field being trained to deviate strongly from the same conformer minimised using\n", | |
| "the reference QM method.\n", | |
| "\n", | |
| "##### TorsionProfile\n", | |
| "\n", | |
| "The torsion profile target penalizes based on the average RMSE between the QM and MM energies evaluated at each scanned\n", | |
| "torsion angle.\n", | |
| "\n", | |
| "To define these targets we at minimum need to provide the QC reference data:" | |
| ], | |
| "metadata": { | |
| "collapsed": false | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 17, | |
| "outputs": [], | |
| "source": [ | |
| "from openff.bespokefit.schema.targets import (\n", | |
| " OptGeoTargetSchema,\n", | |
| " TorsionProfileTargetSchema,\n", | |
| ")\n", | |
| "\n", | |
| "targets = [\n", | |
| " TorsionProfileTargetSchema(reference_data=torsion_drive_result_collection),\n", | |
| " OptGeoTargetSchema(reference_data=optimization_result_collection),\n", | |
| "]" | |
| ], | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%%\n" | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Each of these targets can take a multitude of options which I won't go into detail here, but will be described in much\n", | |
| "more detail in the soon to be published OpenFF Bespoke documentation.\n", | |
| "\n", | |
| "## Training the force field\n", | |
| "\n", | |
| "We are now finally ready to train the force field! We have stated which parameters will be training, we have defined\n", | |
| "the contributions to the loss function (in terms of 'fitting targets') that we will be trying to minimise, and all that\n", | |
| "is left is to tie it all together.\n", | |
| "\n", | |
| "We do this by defining the overall 'optimization schema' - this is a compact python object that encodes every single\n", | |
| "input that will be used to train the force field:" | |
| ], | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%% md\n" | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 18, | |
| "outputs": [], | |
| "source": [ | |
| "from openff.bespokefit.schema.fitting import OptimizationSchema\n", | |
| "from openff.bespokefit.schema.optimizers import ForceBalanceSchema\n", | |
| "\n", | |
| "optimization_schema = OptimizationSchema(\n", | |
| " id=\"linear-alkanes\",\n", | |
| " # Define the force field that we will train\n", | |
| " initial_force_field=initial_force_field_path,\n", | |
| " # Define the optimizer\n", | |
| " optimizer=ForceBalanceSchema(\n", | |
| " max_iterations=50,\n", | |
| " step_convergence_threshold=0.01,\n", | |
| " objective_convergence_threshold=0.1,\n", | |
| " gradient_convergence_threshold=0.1,\n", | |
| " n_criteria=2,\n", | |
| " initial_trust_radius=-1.0,\n", | |
| " ),\n", | |
| " # Define the torsion profile targets to fit against.\n", | |
| " targets=targets,\n", | |
| " # Define the parameters to refit and the priors to place on them.\n", | |
| " target_parameters=target_parameters,\n", | |
| " parameter_settings=parameter_settings,\n", | |
| ")" | |
| ], | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%%\n" | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "as can be seen it is the combination of the starting force field, an optimization engine, the fitting targets, and\n", | |
| "the parameters to be trained. It can trivially be saved to disk:" | |
| ], | |
| "metadata": { | |
| "collapsed": false | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 19, | |
| "outputs": [], | |
| "source": [ | |
| "with open(\"optimization-schema.json\", \"w\") as file:\n", | |
| " file.write(optimization_schema.json())" | |
| ], | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%%\n" | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "allowing anyone to try and reproduce the training that was performed simply be reloading the schema and using it as the\n", | |
| "input.\n", | |
| "\n", | |
| "Training our force field is as simply as calling `optimize`:" | |
| ], | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%% md\n" | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 20, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "making new fb folders in linear-alkanes\n", | |
| "making forcebalance file system in linear-alkanes\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 0%| | 0/3 [00:00<?, ?it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "generating target directory for torsion-1\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "100%|██████████| 3/3 [00:11<00:00, 3.75s/it]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "SMIRNOFF functions will be replaced with cached versions to improve their performance.\n", | |
| "generating target directory for torsion-2\n", | |
| "generating target directory for torsion-3\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| "100%|██████████| 1/1 [00:00<00:00, 40.43it/s]\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "generating target directory for opt-geo-batch-0\n", | |
| "launching forcebalance\n", | |
| "OPT finished in folder /Users/boothros/PyCharmProjects/blog-post/linear-alkanes\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "text/plain": "OptimizationResults(type='general', input_schema=OptimizationSchema(type='general', id='linear-alkanes', initial_force_field='openff_unconstrained-1.3.0.offxml', optimizer=ForceBalanceSchema(type='ForceBalance', max_iterations=50, job_type='optimize', penalty_type='L2', step_convergence_threshold=0.01, objective_convergence_threshold=0.1, gradient_convergence_threshold=0.1, n_criteria=2, eigenvalue_lower_bound=0.01, finite_difference_h=0.01, penalty_additive=1.0, initial_trust_radius=-1.0, minimum_trust_radius=0.05, error_tolerance=1.0, adaptive_factor=0.2, adaptive_damping=1.0, normalize_weights=False, extras={}), targets=[TorsionProfileTargetSchema(weight=1.0, reference_data=TorsionDriveResultCollection(entries={'https://localhost:56700/': [TorsionDriveResult(type='torsion', record_id='1', cmiles='[H:3][C:1]([H:4])([H:5])[C:2]([H:6])([H:7])[H:8]', inchi_key='OTMSDBZUPAUEDD-UHFFFAOYSA-N'), TorsionDriveResult(type='torsion', record_id='2', cmiles='[H:5][C:1]([H:6])([H:7])[C:2]([H:8])([H:9])[C:3]([H:10])([H:11])[C:4]([H:12])([H:13])[H:14]', inchi_key='IJDNQMDRQITEOD-UHFFFAOYSA-N'), TorsionDriveResult(type='torsion', record_id='3', cmiles='[H:5][C:1]([H:6])([H:7])[C:2]([H:8])([H:9])[C:3]([H:10])([H:11])[C:4]([H:12])([H:13])[H:14]', inchi_key='IJDNQMDRQITEOD-UHFFFAOYSA-N')]}, provenance={}), extras={}, type='TorsionProfile', attenuate_weights=True, energy_denominator=1.0, energy_cutoff=10.0), OptGeoTargetSchema(weight=1.0, reference_data=OptimizationResultCollection(entries={'https://api.qcarchive.molssi.org:443/': [OptimizationResult(type='optimization', record_id='25724794', cmiles='[C:1]([C:2]([H:6])([H:7])[H:8])([H:3])([H:4])[H:5]', inchi_key='OTMSDBZUPAUEDD-UHFFFAOYSA-N'), OptimizationResult(type='optimization', record_id='25732484', cmiles='[C:1]([C:3]([C:4]([C:2]([H:8])([H:9])[H:10])([H:13])[H:14])([H:11])[H:12])([H:5])([H:6])[H:7]', inchi_key='IJDNQMDRQITEOD-UHFFFAOYSA-N')]}, provenance={'applied-filters': {'SMILESFilter-0': {'smiles_to_include': ['CC', 'CCCC'], 'smiles_to_exclude': None}}}), extras={}, type='OptGeo', bond_denominator=0.05, angle_denominator=8.0, dihedral_denominator=0.0, improper_denominator=20.0)], parameter_settings=[BondForceSettings(parameter_type=<SmirksType.Bonds: 'Bonds'>, parameter_subtype='Bond', target='k', prior=100.0), BondLengthSettings(parameter_type=<SmirksType.Bonds: 'Bonds'>, parameter_subtype='Bond', target='length', prior=0.1), AngleForceSettings(parameter_type=<SmirksType.Angles: 'Angles'>, parameter_subtype='Angle', target='k', prior=100.0), AngleAngleSettings(parameter_type=<SmirksType.Angles: 'Angles'>, parameter_subtype='Angle', target='angle', prior=20.0), ProperTorsionSettings(parameter_type=<SmirksType.ProperTorsions: 'ProperTorsions'>, parameter_subtype='Proper', target='k', prior=1.0)], target_parameters=[BondSmirks(type='Bonds', smirks='[#6X4:1]-[#6X4:2]', attributes={'k', 'length'}), BondSmirks(type='Bonds', smirks='[#6X4:1]-[#1:2]', attributes={'k', 'length'}), AngleSmirks(type='Angles', smirks='[*:1]~[#6X4:2]-[*:3]', attributes={'k', 'angle'}), AngleSmirks(type='Angles', smirks='[#1:1]-[#6X4:2]-[#1:3]', attributes={'k', 'angle'}), ProperTorsionSmirks(type='ProperTorsions', smirks='[#6X4:1]-[#6X4:2]-[#6X4:3]-[#6X4:4]', attributes={'k1'}), ProperTorsionSmirks(type='ProperTorsions', smirks='[#1:1]-[#6X4:2]-[#6X4:3]-[#6X4:4]', attributes={'k1'}), ProperTorsionSmirks(type='ProperTorsions', smirks='[#1:1]-[#6X4:2]-[#6X4:3]-[#1:4]', attributes={'k1'})]), provenance={'forcebalance': 'v1.9.0', 'openff.toolkit': '0.9.2', 'openeye.oechem': '2021.1.1', 'openeye.oequacpac': '2021.1.1', 'openeye.oeomega': '2021.1.1'}, status=<Status.Complete: 'COMPLETE'>, refit_force_field='<?xml version=\"1.0\" encoding=\"utf-8\"?>\\n<SMIRNOFF version=\"0.3\" aromaticity_model=\"OEAroModel_MDL\">\\n\\t<Author>The Open Force Field Initiative</Author>\\n\\t<Date>2020-10-21</Date>\\n\\t<Bonds version=\"0.3\" potential=\"harmonic\" fractional_bondorder_method=\"AM1-Wiberg\" fractional_bondorder_interpolation=\"linear\">\\n\\t\\t<Bond smirks=\"[#6X4:1]-[#6X4:2]\" id=\"b1\" length=\"3.370214283401 * angstrom\" k=\"442.2000803384 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6X4:1]-[#6X3:2]\" id=\"b2\" length=\"1.501037244555 * angstrom\" k=\"601.2284108955 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6X4:1]-[#6X3:2]=[#8X1+0]\" id=\"b3\" length=\"1.521644911471 * angstrom\" k=\"663.5796689652 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6X3:1]-[#6X3:2]\" id=\"b4\" length=\"1.452604804913 * angstrom\" k=\"541.7488836693 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6X3:1]:[#6X3:2]\" id=\"b5\" length=\"1.390518677532 * angstrom\" k=\"689.2732730871 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6X3:1]=[#6X3:2]\" id=\"b6\" length=\"1.376004247979 * angstrom\" k=\"785.4733197609 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6:1]-[#7:2]\" id=\"b7\" length=\"1.461531761708 * angstrom\" k=\"716.4576464043 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6X3:1]-[#7X3:2]\" id=\"b8\" length=\"1.390168708029 * angstrom\" k=\"768.655536364 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6X4:1]-[#7X3:2]-[#6X3]=[#8X1+0]\" id=\"b9\" length=\"1.449346371125 * angstrom\" k=\"704.4966056244 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6X3:1](=[#8X1+0])-[#7X3:2]\" id=\"b10\" length=\"1.375574777154 * angstrom\" k=\"958.7048267044 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6X3:1]-[#7X2:2]\" id=\"b11\" length=\"1.38106431582 * angstrom\" k=\"708.3989749935 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6X3:1]:[#7X2,#7X3+1:2]\" id=\"b12\" length=\"1.332172844737 * angstrom\" k=\"831.8762885839 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6X3:1]=[#7X2,#7X3+1:2]\" id=\"b13\" length=\"1.300220697757 * angstrom\" k=\"987.2600166236 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6:1]-[#8:2]\" id=\"b14\" length=\"1.429330393438 * angstrom\" k=\"664.3946131079 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6X3:1]-[#8X1-1:2]\" id=\"b14a\" length=\"1.285001656711 * angstrom\" k=\"638.4370163325 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6X4:1]-[#8X2H0:2]\" id=\"b15\" length=\"1.428928113679 * angstrom\" k=\"736.4755257787 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6X3:1]-[#8X2:2]\" id=\"b16\" length=\"1.368612166925 * angstrom\" k=\"719.7194400131 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6X3:1]-[#8X2H1:2]\" id=\"b17\" length=\"1.369468156503 * angstrom\" k=\"790.2138705471 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6X3a:1]-[#8X2H0:2]\" id=\"b18\" length=\"1.369247314096 * angstrom\" k=\"827.3303999397 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6X3:1](=[#8X1])-[#8X2H0:2]\" id=\"b19\" length=\"1.350125361867 * angstrom\" k=\"616.6889982732 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6:1]=[#8X1+0,#8X2+1:2]\" id=\"b20\" length=\"1.228792915057 * angstrom\" k=\"1176.748901628 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6X3:1](~[#8X1])~[#8X1:2]\" id=\"b21\" length=\"1.261437823232 * angstrom\" k=\"1157.796595057 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6X3:1]~[#8X2+1:2]~[#6X3]\" id=\"b22\" length=\"1.360087778512 * angstrom\" k=\"1088.75931445 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6X2:1]-[#6:2]\" id=\"b23\" length=\"1.426031948982 * angstrom\" k=\"1640.574559316 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6X2:1]-[#6X4:2]\" id=\"b24\" length=\"1.453716802068 * angstrom\" k=\"800.0 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6X2:1]=[#6X3:2]\" id=\"b25\" length=\"1.313916554213 * angstrom\" k=\"1161.375438886 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6:1]#[#7:2]\" id=\"b26\" length=\"1.166493990729 * angstrom\" k=\"1622.571734028 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6X2:1]#[#6X2:2]\" id=\"b27\" length=\"1.214267533917 * angstrom\" k=\"1000.0 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6X2:1]-[#8X2:2]\" id=\"b28\" length=\"1.321538910206 * angstrom\" k=\"700.0497029132 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6X2:1]-[#7:2]\" id=\"b29\" length=\"1.347314286573 * angstrom\" k=\"1033.464402026 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6X2:1]=[#7:2]\" id=\"b30\" length=\"1.196112346392 * angstrom\" k=\"1205.62768674 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#16:1]=[#6:2]\" id=\"b31a\" length=\"1.671311106175 * angstrom\" k=\"593.8255795778 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6X2:1]=[#16:2]\" id=\"b31\" length=\"1.580136796038 * angstrom\" k=\"971.078275835 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#7:1]-[#7:2]\" id=\"b32\" length=\"1.395820226577 * angstrom\" k=\"835.8939107268 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#7X3:1]-[#7X2:2]\" id=\"b33\" length=\"1.348040796856 * angstrom\" k=\"787.9587602802 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#7X2:1]-[#7X2:2]\" id=\"b34\" length=\"1.351789596719 * angstrom\" k=\"684.0446604673 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#7:1]:[#7:2]\" id=\"b35\" length=\"1.306827001896 * angstrom\" k=\"731.7076511835 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#7:1]=[#7:2]\" id=\"b36\" length=\"1.28836275019 * angstrom\" k=\"723.6243426279 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#7+1:1]=[#7-1:2]\" id=\"b36a\" length=\"1.216477474649 * angstrom\" k=\"765.606596983 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#7:1]#[#7:2]\" id=\"b37\" length=\"1.156918673037 * angstrom\" k=\"760.3057793033 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#7:1]-[#8X2:2]\" id=\"b38\" length=\"1.409351807267 * angstrom\" k=\"585.4468253974 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#7:1]~[#8X1:2]\" id=\"b39\" length=\"1.236596022356 * angstrom\" k=\"731.6290941822 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#8X2:1]-[#8X2:2]\" id=\"b40\" length=\"1.451203938761 * angstrom\" k=\"600.0627582923 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#16:1]-[#6:2]\" id=\"b41\" length=\"1.81 * angstrom\" k=\"474.0 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#16:1]-[#1:2]\" id=\"b42\" length=\"1.350439509509 * angstrom\" k=\"598.1961543498 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#16:1]-[#16:2]\" id=\"b43\" length=\"2.088681010671 * angstrom\" k=\"319.3489883071 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#16:1]-[#9:2]\" id=\"b44\" length=\"1.6 * angstrom\" k=\"750.0 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#16:1]-[#17:2]\" id=\"b45\" length=\"2.090689740082 * angstrom\" k=\"460.4266106166 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#16:1]-[#35:2]\" id=\"b46\" length=\"2.236089315911 * angstrom\" k=\"337.0828021823 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#16:1]-[#53:2]\" id=\"b47\" length=\"2.6 * angstrom\" k=\"150.0 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#16X2,#16X1-1,#16X3+1:1]-[#6X4:2]\" id=\"b48\" length=\"1.832604780293 * angstrom\" k=\"462.4390471119 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#16X2,#16X1-1,#16X3+1:1]-[#6X3:2]\" id=\"b49\" length=\"1.764695668665 * angstrom\" k=\"557.107757113 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#16X2:1]-[#7:2]\" id=\"b50\" length=\"1.685967252936 * angstrom\" k=\"601.0291285501 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#16X2:1]-[#8X2:2]\" id=\"b51\" length=\"1.670741273642 * angstrom\" k=\"514.4207231886 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#16X2:1]=[#8X1,#7X2:2]\" id=\"b52\" length=\"1.466630613191 * angstrom\" k=\"605.9448106876 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#16X4,#16X3!+1:1]-[#6:2]\" id=\"b53\" length=\"1.83094269297 * angstrom\" k=\"514.6069966574 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#16X4,#16X3:1]~[#7:2]\" id=\"b54\" length=\"1.745684938763 * angstrom\" k=\"594.5780213075 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#16X4,#16X3:1]-[#8X2:2]\" id=\"b55\" length=\"1.65750446814 * angstrom\" k=\"610.0430744451 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#16X4,#16X3:1]~[#8X1:2]\" id=\"b56\" length=\"1.477542175281 * angstrom\" k=\"1081.335593259 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#15:1]-[#1:2]\" id=\"b58\" length=\"1.409632139658 * angstrom\" k=\"400.1299638072 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#15:1]~[#6:2]\" id=\"b59\" length=\"1.834505699142 * angstrom\" k=\"322.3694911153 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#15:1]-[#7:2]\" id=\"b60\" length=\"1.75691942977 * angstrom\" k=\"626.4135650071 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#15:1]=[#7:2]\" id=\"b61\" length=\"1.593021554161 * angstrom\" k=\"820.4422422288 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#15:1]~[#8X2:2]\" id=\"b62\" length=\"1.617759068105 * angstrom\" k=\"512.3988511673 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#15:1]~[#8X1:2]\" id=\"b63\" length=\"1.488230653008 * angstrom\" k=\"1106.254480479 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#16:1]-[#15:2]\" id=\"b65\" length=\"2.136319123687 * angstrom\" k=\"298.0152402791 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#15:1]=[#16X1:2]\" id=\"b66\" length=\"1.98 * angstrom\" k=\"460.0 * angstrom**-2 * mole**-1 * kilocalorie\"></Bond>\\n\\t\\t<Bond smirks=\"[#6:1]-[#9:2]\" id=\"b67\" length=\"1.35676207799 * 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epsilon=\"0.015 * mole**-1 * kilocalorie\" id=\"n7\" rmin_half=\"1.459 * angstrom\"></Atom>\\n\\t\\t<Atom smirks=\"[#1:1]-[#6X3]~[#7,#8,#9,#16,#17,#35]\" epsilon=\"0.015 * mole**-1 * kilocalorie\" id=\"n8\" rmin_half=\"1.409 * angstrom\"></Atom>\\n\\t\\t<Atom smirks=\"[#1:1]-[#6X3](~[#7,#8,#9,#16,#17,#35])~[#7,#8,#9,#16,#17,#35]\" epsilon=\"0.015 * mole**-1 * kilocalorie\" id=\"n9\" rmin_half=\"1.359 * angstrom\"></Atom>\\n\\t\\t<Atom smirks=\"[#1:1]-[#6X2]\" epsilon=\"0.015 * mole**-1 * kilocalorie\" id=\"n10\" rmin_half=\"1.459 * angstrom\"></Atom>\\n\\t\\t<Atom smirks=\"[#1:1]-[#7]\" epsilon=\"0.0157 * mole**-1 * kilocalorie\" id=\"n11\" rmin_half=\"0.6 * angstrom\"></Atom>\\n\\t\\t<Atom smirks=\"[#1:1]-[#8]\" epsilon=\"5.27e-05 * mole**-1 * kilocalorie\" id=\"n12\" rmin_half=\"0.3 * angstrom\"></Atom>\\n\\t\\t<Atom smirks=\"[#1:1]-[#16]\" epsilon=\"0.0157 * mole**-1 * kilocalorie\" id=\"n13\" rmin_half=\"0.6 * angstrom\"></Atom>\\n\\t\\t<Atom smirks=\"[#6:1]\" epsilon=\"0.086 * mole**-1 * kilocalorie\" id=\"n14\" rmin_half=\"1.908 * angstrom\"></Atom>\\n\\t\\t<Atom smirks=\"[#6X2:1]\" epsilon=\"0.21 * mole**-1 * kilocalorie\" id=\"n15\" rmin_half=\"1.908 * angstrom\"></Atom>\\n\\t\\t<Atom smirks=\"[#6X4:1]\" epsilon=\"0.1094 * mole**-1 * kilocalorie\" id=\"n16\" rmin_half=\"1.908 * angstrom\"></Atom>\\n\\t\\t<Atom smirks=\"[#8:1]\" epsilon=\"0.21 * mole**-1 * kilocalorie\" id=\"n17\" rmin_half=\"1.6612 * angstrom\"></Atom>\\n\\t\\t<Atom smirks=\"[#8X2H0+0:1]\" epsilon=\"0.17 * mole**-1 * kilocalorie\" id=\"n18\" rmin_half=\"1.6837 * angstrom\"></Atom>\\n\\t\\t<Atom smirks=\"[#8X2H1+0:1]\" epsilon=\"0.2104 * mole**-1 * kilocalorie\" id=\"n19\" rmin_half=\"1.721 * angstrom\"></Atom>\\n\\t\\t<Atom smirks=\"[#7:1]\" epsilon=\"0.17 * mole**-1 * kilocalorie\" id=\"n20\" rmin_half=\"1.824 * angstrom\"></Atom>\\n\\t\\t<Atom smirks=\"[#16:1]\" epsilon=\"0.25 * mole**-1 * kilocalorie\" id=\"n21\" rmin_half=\"2.0 * angstrom\"></Atom>\\n\\t\\t<Atom smirks=\"[#15:1]\" epsilon=\"0.2 * mole**-1 * kilocalorie\" id=\"n22\" rmin_half=\"2.1 * angstrom\"></Atom>\\n\\t\\t<Atom smirks=\"[#9:1]\" epsilon=\"0.061 * mole**-1 * kilocalorie\" id=\"n23\" rmin_half=\"1.75 * angstrom\"></Atom>\\n\\t\\t<Atom smirks=\"[#17:1]\" epsilon=\"0.265 * mole**-1 * kilocalorie\" id=\"n24\" rmin_half=\"1.948 * angstrom\"></Atom>\\n\\t\\t<Atom smirks=\"[#35:1]\" epsilon=\"0.32 * mole**-1 * kilocalorie\" id=\"n25\" rmin_half=\"2.22 * angstrom\"></Atom>\\n\\t\\t<Atom smirks=\"[#53:1]\" epsilon=\"0.4 * mole**-1 * kilocalorie\" id=\"n26\" rmin_half=\"2.35 * angstrom\"></Atom>\\n\\t\\t<Atom smirks=\"[#3+1:1]\" epsilon=\"0.0279896 * mole**-1 * kilocalorie\" id=\"n27\" rmin_half=\"1.025 * angstrom\"></Atom>\\n\\t\\t<Atom smirks=\"[#11+1:1]\" epsilon=\"0.0874393 * mole**-1 * kilocalorie\" id=\"n28\" rmin_half=\"1.369 * angstrom\"></Atom>\\n\\t\\t<Atom smirks=\"[#19+1:1]\" epsilon=\"0.1936829 * mole**-1 * kilocalorie\" id=\"n29\" rmin_half=\"1.705 * angstrom\"></Atom>\\n\\t\\t<Atom smirks=\"[#37+1:1]\" epsilon=\"0.3278219 * mole**-1 * kilocalorie\" id=\"n30\" rmin_half=\"1.813 * angstrom\"></Atom>\\n\\t\\t<Atom smirks=\"[#55+1:1]\" epsilon=\"0.4065394 * mole**-1 * kilocalorie\" id=\"n31\" rmin_half=\"1.976 * angstrom\"></Atom>\\n\\t\\t<Atom smirks=\"[#9X0-1:1]\" epsilon=\"0.003364 * mole**-1 * kilocalorie\" id=\"n32\" rmin_half=\"2.303 * angstrom\"></Atom>\\n\\t\\t<Atom smirks=\"[#17X0-1:1]\" epsilon=\"0.035591 * mole**-1 * kilocalorie\" id=\"n33\" rmin_half=\"2.513 * angstrom\"></Atom>\\n\\t\\t<Atom smirks=\"[#35X0-1:1]\" epsilon=\"0.0586554 * mole**-1 * kilocalorie\" id=\"n34\" rmin_half=\"2.608 * angstrom\"></Atom>\\n\\t\\t<Atom smirks=\"[#53X0-1:1]\" epsilon=\"0.0536816 * mole**-1 * kilocalorie\" id=\"n35\" rmin_half=\"2.86 * angstrom\"></Atom>\\n\\t</vdW>\\n\\t<Electrostatics version=\"0.3\" scale12=\"0.0\" scale13=\"0.0\" scale14=\"0.8333333333\" scale15=\"1.0\" cutoff=\"9.0 * angstrom\" switch_width=\"0.0 * angstrom\" method=\"PME\"></Electrostatics>\\n\\t<LibraryCharges version=\"0.3\">\\n\\t\\t<LibraryCharge smirks=\"[#3+1:1]\" charge1=\"1.0 * elementary_charge\" name=\"Li+\"></LibraryCharge>\\n\\t\\t<LibraryCharge smirks=\"[#11+1:1]\" charge1=\"1.0 * elementary_charge\" name=\"Na+\"></LibraryCharge>\\n\\t\\t<LibraryCharge smirks=\"[#19+1:1]\" charge1=\"1.0 * elementary_charge\" name=\"K+\"></LibraryCharge>\\n\\t\\t<LibraryCharge smirks=\"[#37+1:1]\" charge1=\"1.0 * elementary_charge\" name=\"Rb+\"></LibraryCharge>\\n\\t\\t<LibraryCharge smirks=\"[#55+1:1]\" charge1=\"1.0 * elementary_charge\" name=\"Cs+\"></LibraryCharge>\\n\\t\\t<LibraryCharge smirks=\"[#9X0-1:1]\" charge1=\"-1.0 * elementary_charge\" name=\"F-\"></LibraryCharge>\\n\\t\\t<LibraryCharge smirks=\"[#17X0-1:1]\" charge1=\"-1.0 * elementary_charge\" name=\"Cl-\"></LibraryCharge>\\n\\t\\t<LibraryCharge smirks=\"[#35X0-1:1]\" charge1=\"-1.0 * elementary_charge\" name=\"Br-\"></LibraryCharge>\\n\\t\\t<LibraryCharge smirks=\"[#53X0-1:1]\" charge1=\"-1.0 * elementary_charge\" name=\"I-\"></LibraryCharge>\\n\\t</LibraryCharges>\\n\\t<ToolkitAM1BCC version=\"0.3\"></ToolkitAM1BCC>\\n</SMIRNOFF>')" | |
| }, | |
| "execution_count": 20, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "from openff.bespokefit.optimizers.forcebalance import ForceBalanceOptimizer\n", | |
| "\n", | |
| "results = ForceBalanceOptimizer.optimize(optimization_schema)\n", | |
| "results" | |
| ], | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%%\n" | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Under the hood, the OpenFF Bespoke is creating all the required input files that will be required, and then calling\n", | |
| "out to the optimization engine (in this case ForceBalace) to perform the actual training." | |
| ], | |
| "metadata": { | |
| "collapsed": false | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Inspecting the final parameters\n", | |
| "\n", | |
| "The results object returned after training the force field contains information collected during the training as well as\n", | |
| "the final force field parameters.\n", | |
| "\n", | |
| "Let's print out our initial and final parameters to convince ourselves that something has actually changed!" | |
| ], | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%% md\n" | |
| } | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "final_force_field = ForceField(results.refit_force_field)\n", | |
| "\n", | |
| "for handler_name in [\"Bonds\", \"Angles\", \"ProperTorsions\"]:\n", | |
| "\n", | |
| " print(handler_name.upper() + \"\\n\")\n", | |
| "\n", | |
| " for smirks in coverage[handler_name]:\n", | |
| "\n", | |
| " initial_parameter = initial_force_field[handler_name].parameters[smirks]\n", | |
| " final_parameter = final_force_field[handler_name].parameters[smirks]\n", | |
| "\n", | |
| " print(smirks)\n", | |
| " print(f\"INITIAL: {initial_parameter}\")\n", | |
| " print(f\"FINAL: {final_parameter}\")\n", | |
| "\n", | |
| " print(\"\\n\")" | |
| ], | |
| "metadata": { | |
| "collapsed": false, | |
| "pycharm": { | |
| "name": "#%%\n" | |
| } | |
| }, | |
| "execution_count": 21, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "BONDS\n", | |
| "\n", | |
| "[#6X4:1]-[#6X4:2]\n", | |
| "INITIAL: <BondType with smirks: [#6X4:1]-[#6X4:2] id: b1 length: 1.520980132854 A k: 517.2187207483 kcal/(A**2 mol) >\n", | |
| "FINAL: <BondType with smirks: [#6X4:1]-[#6X4:2] id: b1 length: 3.370214283401 A k: 442.2000803384 kcal/(A**2 mol) >\n", | |
| "[#6X4:1]-[#1:2]\n", | |
| "INITIAL: <BondType with smirks: [#6X4:1]-[#1:2] id: b83 length: 1.093910524997 A k: 754.0714751826 kcal/(A**2 mol) >\n", | |
| "FINAL: <BondType with smirks: [#6X4:1]-[#1:2] id: b83 length: 4.934017052951 A k: 726.8928453096 kcal/(A**2 mol) >\n", | |
| "\n", | |
| "\n", | |
| "ANGLES\n", | |
| "\n", | |
| "[*:1]~[#6X4:2]-[*:3]\n", | |
| "INITIAL: <AngleType with smirks: [*:1]~[#6X4:2]-[*:3] angle: 113.6569396169 deg k: 99.23399412421 kcal/(mol rad**2) id: a1 >\n", | |
| "FINAL: <AngleType with smirks: [*:1]~[#6X4:2]-[*:3] angle: 128.0104469174 deg k: 267.1171472352 kcal/(mol rad**2) id: a1 >\n", | |
| "[#1:1]-[#6X4:2]-[#1:3]\n", | |
| "INITIAL: <AngleType with smirks: [#1:1]-[#6X4:2]-[#1:3] angle: 114.294084683 deg k: 66.55229431401 kcal/(mol rad**2) id: a2 >\n", | |
| "FINAL: <AngleType with smirks: [#1:1]-[#6X4:2]-[#1:3] angle: 131.7093320421 deg k: 182.5003722512 kcal/(mol rad**2) id: a2 >\n", | |
| "\n", | |
| "\n", | |
| "PROPERTORSIONS\n", | |
| "\n", | |
| "[#6X4:1]-[#6X4:2]-[#6X4:3]-[#6X4:4]\n", | |
| "INITIAL: <ProperTorsionType with smirks: [#6X4:1]-[#6X4:2]-[#6X4:3]-[#6X4:4] periodicity1: 3 periodicity2: 2 periodicity3: 1 phase1: 0.0 deg phase2: 180.0 deg phase3: 180.0 deg id: t2 k1: 0.1164348133257 kcal/mol k2: 0.2491194267913 kcal/mol k3: 0.295757514793 kcal/mol idivf1: 1.0 idivf2: 1.0 idivf3: 1.0 >\n", | |
| "FINAL: <ProperTorsionType with smirks: [#6X4:1]-[#6X4:2]-[#6X4:3]-[#6X4:4] periodicity1: 3 periodicity2: 2 periodicity3: 1 phase1: 0.0 deg phase2: 180.0 deg phase3: 180.0 deg id: t2 k1: 0.9113798564659 kcal/mol k2: 0.2491194267913 kcal/mol k3: 0.295757514793 kcal/mol idivf1: 1.0 idivf2: 1.0 idivf3: 1.0 >\n", | |
| "[#1:1]-[#6X4:2]-[#6X4:3]-[#6X4:4]\n", | |
| "INITIAL: <ProperTorsionType with smirks: [#1:1]-[#6X4:2]-[#6X4:3]-[#6X4:4] periodicity1: 3 phase1: 0.0 deg id: t4 k1: 0.08414560911761 kcal/mol idivf1: 1.0 >\n", | |
| "FINAL: <ProperTorsionType with smirks: [#1:1]-[#6X4:2]-[#6X4:3]-[#6X4:4] periodicity1: 3 phase1: 0.0 deg id: t4 k1: 0.7717343515832 kcal/mol idivf1: 1.0 >\n", | |
| "[#1:1]-[#6X4:2]-[#6X4:3]-[#1:4]\n", | |
| "INITIAL: <ProperTorsionType with smirks: [#1:1]-[#6X4:2]-[#6X4:3]-[#1:4] periodicity1: 3 phase1: 0.0 deg id: t3 k1: 0.2005394316088 kcal/mol idivf1: 1.0 >\n", | |
| "FINAL: <ProperTorsionType with smirks: [#1:1]-[#6X4:2]-[#6X4:3]-[#1:4] periodicity1: 3 phase1: 0.0 deg id: t3 k1: 0.3597784410686 kcal/mol idivf1: 1.0 >\n", | |
| "\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Conclusions\n", | |
| "\n", | |
| "And that's all there is to it! As I've hopefully proved here, the ecosystem provided by the Open Force Field consortium\n", | |
| "enables entire force fields to be re-trained from within a single jupyter notebook.\n", | |
| "\n", | |
| "It is an ecosystem that is rapidly evolving, expanding, and improving, and ultimately aims to allow anyone to build\n", | |
| "their own force fields without requiring a whole team of researchers. If you found that something was unclear or did not\n", | |
| "work as you would expect, the team would love to hear from you! Please reach out to support@openforcefield.org if you\n", | |
| "have any feedback or, alternatively, open an issue on one of the consortiums [many GitHub repositories](\n", | |
| "https://github.com/openforcefield)." | |
| ], | |
| "metadata": { | |
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| "display_name": "Python 3", | |
| "language": "python", | |
| "name": "python3" | |
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| "version": "2.7.6" | |
| } | |
| }, | |
| "nbformat": 4, | |
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| } |
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