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

Abstract

Here, 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.

Authors:
; ; ; ;
Publication Date:
Research Org.:
Ames Laboratory (AMES), Ames, IA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1347393
Report Number(s):
IS-J-9104
Journal ID: ISSN 1549-9596
Grant/Contract Number:  
AC02-07CH11358
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of Chemical Information and Modeling
Additional Journal Information:
Journal Volume: 57; Journal Issue: 3; Journal ID: ISSN 1549-9596
Publisher:
American Chemical Society
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; 97 MATHEMATICS AND COMPUTING

Citation Formats

Zahariev, Federico, De Silva, Nuwan, Gordon, Mark S., Windus, Theresa L., and Dick-Perez, Marilu. ParFit: A Python-Based Object-Oriented Program for Fitting Molecular Mechanics Parameters to ab Initio Data. United States: N. p., 2017. Web. doi:10.1021/acs.jcim.6b00654.
Zahariev, Federico, De Silva, Nuwan, Gordon, Mark S., Windus, Theresa L., & Dick-Perez, Marilu. ParFit: A Python-Based Object-Oriented Program for Fitting Molecular Mechanics Parameters to ab Initio Data. United States. doi:10.1021/acs.jcim.6b00654.
Zahariev, Federico, De Silva, Nuwan, Gordon, Mark S., Windus, Theresa L., and Dick-Perez, Marilu. Thu . "ParFit: A Python-Based Object-Oriented Program for Fitting Molecular Mechanics Parameters to ab Initio Data". United States. doi:10.1021/acs.jcim.6b00654. https://www.osti.gov/servlets/purl/1347393.
@article{osti_1347393,
title = {ParFit: A Python-Based Object-Oriented Program for Fitting Molecular Mechanics Parameters to ab Initio Data},
author = {Zahariev, Federico and De Silva, Nuwan and Gordon, Mark S. and Windus, Theresa L. and Dick-Perez, Marilu},
abstractNote = {Here, 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.},
doi = {10.1021/acs.jcim.6b00654},
journal = {Journal of Chemical Information and Modeling},
number = 3,
volume = 57,
place = {United States},
year = {Thu Feb 23 00:00:00 EST 2017},
month = {Thu Feb 23 00:00:00 EST 2017}
}

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