openforcefield / openff-toolkit

The Open Forcefield Toolkit provides implementations of the SMIRNOFF format, parameterization engine, and other tools. Documentation available at http://open-forcefield-toolkit.readthedocs.io
http://openforcefield.org
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The Open Force Field toolkit

The Open Force Field Toolkit, built by the Open Force Field Initiative, is a Python toolkit for the development and application of modern molecular mechanics force fields based on direct chemical perception and rigorous statistical parameterization methods.

The toolkit currently covers two main areas we have committed to stably maintain throughout their lifetimes:

Note: Prior to version 0.9.0, this toolkit and its associated repository were named openforcefield and used different import paths. For details on this change and migration instructions, see the release notes of version 0.9.0.

Documentation

Documentation for the Open Force Field Toolkit is hosted at readthedocs. Example notebooks are available in the examples/ directory and also hosted on the Open Force Field website.

How to cite

Please cite the OpenFF Toolkit using the Zenodo record of the latest release or the version that was used. The BibTeX reference of the latest release can be found here.

Installation

The Open Force Field Toolkit (openff-toolkit) is a Python toolkit, and supports Python 3.9 through 3.11.

Installing via Mamba/Conda

Detailed installation instructions can be found here.

Force Fields

Two major force field development efforts have been undertaken by the Open Force Field Initiative, with results hosted in separate repositories.

Force fields from both of these packages are available in their respective GitHub repositories and also as conda packages. Tables detailing the individual file names/versions within these force field lines are in the README of each repository. By default, installing the Open Force Field toolkit using conda or the single-file toolkit installers will also install these conda packages. A plugin architecture is provided for other force field developers to produce python/conda packages that can be imported by the Open Force Field Toolkit as well.

Toolkit features

The SMIRKS Native Open Force Field (SMIRNOFF) format

This repository provides tools for using the SMIRKS Native Open Force Field (SMIRNOFF) specification, which currently supports an XML representation for force field definition files.

By convention, files containing XML representations of SMIRNOFF force fields carry .offxml extensions.

Example SMIRNOFF .offxml force field definitions can be found in openff/toolkit/data/test_forcefields/. These force fields are for testing only, and we neither record versions of these files, nor do we guarantee their correctness or completeness.

Working with SMIRNOFF parameter sets

SMIRNOFF force fields can be parsed by the ForceField class, which offers methods including create_openmm_system for exporting to OpenMM and create_interchange for exporting to other formats (GROMACS, Amber, LAMMPS) via Interchange.

# Load a molecule into the OpenFF Molecule object
from openff.toolkit import Molecule
from openff.toolkit.utils import get_data_file_path
sdf_file_path = get_data_file_path('molecules/ethanol.sdf')
molecule = Molecule.from_file(sdf_file_path)

# Create an OpenFF Topology object from the molecule
from openff.toolkit import Topology
topology = Topology.from_molecules(molecule)

# Load the latest OpenFF force field release: version 2.1.0, codename "Sage"
from openff.toolkit import ForceField
forcefield = ForceField('openff-2.1.0.offxml')

# Create an OpenMM system representing the molecule with SMIRNOFF-applied parameters
openmm_system = forcefield.create_openmm_system(topology)

# Create an Interchange object for representations in other formats
interchange = forcefield.create_interchange(topology)

Detailed examples of using SMIRNOFF with the toolkit can be found in the documentation.

Frequently asked questions (FAQ)

See FAQ.md for answers to a variety of common problems, such as:

Contributors

For a partial list of contributors, see the GitHub Contributors page. Others whose work constitutes significant contributions but did not make it into the git history include Shuzhe Wang.