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)
- OSTI Identifier:
- 1347393
- Report Number(s):
- IS-J-9104
Journal ID: ISSN 1549-9596
- Grant/Contract Number:
- AC02-07CH11358
- Resource Type:
- 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. https://doi.org/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. https://doi.org/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}
}
Web of Science
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