ParFit: A Python-Based Object-Oriented Program for Fitting Molecular Mechanics Parameters to ab Initio Data

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 parameter...

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Bibliographic Details
Published in:	Journal of chemical information and modeling Vol. 57; no. 3; pp. 391 - 396
Main Authors:	Zahariev, Federico, De Silva, Nuwan, Gordon, Mark S, Windus, Theresa L, Pérez García, Marilú
Format:	Journal Article
Language:	English
Published:	United States 27.03.2017
Subjects:	Algorithms Programming Languages Quantum Theory Statistics as Topic - methods
ISSN:	1549-960X
Online Access:	Get more information
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Summary:	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 ( https://github.com/fzahari/ParFit ).
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1549-960X
DOI:	10.1021/acs.jcim.6b00654