Title
ParFit: A Python-Based Object-Oriented Program for Fitting Molecular Mechanics Parameters to ab Initio Data.
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 (https://github.com/fzahari/ParFit).
Year
DOI
Venue
2017
10.1021/acs.jcim.6b00654
JOURNAL OF CHEMICAL INFORMATION AND MODELING
Field
DocType
Volume
Object-oriented programming,Parametrization,Computer science,Antisymmetric relation,Computational science,Ab initio,Transferability,Multi-core processor,Python (programming language),Genetic algorithm
Journal
57
Issue
ISSN
Citations 
3
1549-9596
1
PageRank 
References 
Authors
0.35
11
5
Name
Order
Citations
PageRank
Federico Zahariev110.35
Nuwan De Silva210.69
Mark S. Gordon328325.73
Theresa L. Windus422930.66
Marilu Dick-Perez510.35