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Reason this release was yanked:

Non-maintainer uploaded package to pypi without coordinating with maintainers, pypi package ownership was transferred amicably to maintainers. Pypi releases of openforcefield software may be available at a later date, but issues with unavailable dependencies need to be resolved first.

Project description

OpenFF Recharge

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OpenFF Recharge aims to provide a comprehensive suite of tools for training the partial charges of molecules against quantum chemical electrostatic potential (ESP) and electric field data.

A focus is given to training 'charge-correction models' similar to the popular AM1BCC charge model, but support for other methods such as deriving RESP charges or training virtual sites on top of existing partial charges is also supported.

Warning - although a significant effort has been made to ensure the scientific validity of this framework (especially the hand-converted AM1BCC parameters), it is still under heavy development and much care should be taken when using it in production work.

Getting Started

To start using this framework we recommend looking over the documentation, especially the installation and quick start guides.

How to Cite

Please cite OpenFF Recharge 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.

Features

The framework currently supports:

  • Generating QC ESP and electric field data

    • directly by interfacing with the Psi4 quantum chemical code
    • from wavefunctions stored within a QCFractal instance, including the QCArchive
  • Defining new charge models that contain

    • base QC (e.g. AM1 charges) or tabulated library / RESP charges
    • bond-charge corrections
    • virtual sites
  • A SMARTS port of the AM1BCC charge model

  • Generating RESP charges for multi-conformer molecules

  • Training charge(-correction) parameters by

    • the normal linear least squares method (fixed v-site geometries only)
    • gradient descent using pytorch or numpy
    • Bayesian methods using frameworks like pyro

License

The main package is release under the MIT license.

Copyright

Copyright (c) 2020, Open Force Field Consortium

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