ParFit: A Python-Based Object-Oriented Program for Fitting Molecular Mechanics Parameters to ab Initio Data Gordon, Mark Windus, Theresa Gordon, Mark Windus, Theresa Pérez García, Marilú
dc.contributor.department Chemistry
dc.contributor.department Ames Laboratory 2022-03-01T22:37:02Z 2022-03-01T22:37:02Z 2017-03-27
dc.description.abstract A newly created object-oriented program for automating the process of fitting molecular-mechanics parameters to ab initio data, termed ParFit, is presented. ParFit uses a hybrid of deterministic and stochastic genetic algorithms. ParFit can simultaneously handle several molecular-mechanics parameters in multiple molecules and can also apply symmetric and antisymmetric constraints on the optimized parameters. The simultaneous handling of several molecules enhances the transferability of the fitted parameters. ParFit is written in Python, uses a rich set of standard and nonstandard Python libraries, and can be run in parallel on multicore computer systems. As an example, a series of phosphine oxides, important for metal extraction chemistry, are parametrized using ParFit. ParFit is in an open source program available for free on GitHub (
dc.description.comments This document is the unedited Author’s version of a Submitted Work that was subsequently accepted for publication in Journal of Chemical Information and Modeling, copyright © 2017 American Chemical Society after peer review. To access the final edited and published work see DOI: 10.1021/acs.jcim.6b00654. Posted with permission.
dc.language.iso en_US
dc.publisher American Chemical Society
dc.source.uri *
dc.title ParFit: A Python-Based Object-Oriented Program for Fitting Molecular Mechanics Parameters to ab Initio Data
dc.type Article
dspace.entity.type Publication
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