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Force‐field parametrization based on radial and energy distribution functions
Author(s) -
Chiba Shuntaro,
Okuno Yasushi,
Honma Teruki,
Ikeguchi Mitsunori
Publication year - 2019
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.26035
Subject(s) - pairwise comparison , parametrization (atmospheric modeling) , force field (fiction) , rdf , range (aeronautics) , radial distribution function , computer science , field (mathematics) , statistical physics , energy (signal processing) , granularity , distribution function , algorithm , mathematics , physics , computational chemistry , chemistry , statistics , materials science , quantum mechanics , molecular dynamics , artificial intelligence , pure mathematics , semantic web , composite material , radiative transfer , operating system
We propose a novel force‐field‐parametrization procedure that fits the parameters of potential functions in a manner that the pair distribution function (DF) of molecules derived from candidate parameters can reproduce the given target DF. Conventionally, approaches to minimize the difference between the candidate and target DFs employ radial DFs (RDF). RDF itself has been reported to be insufficient for uniquely identifying the parameters of a molecule. To overcome the weakness, we introduce energy DF (EDF) as a target DF, which describes the distribution of the pairwise energy of molecules. We found that the EDF responds more sensitively to a small perturbation in the pairwise potential parameters and provides better fitting accuracy compared to that of RDF. These findings provide valuable insights into a wide range of coarse graining methods, which determine parameters using information obtained from a higher‐level calculation than that of the developed force field. © 2019 The Authors. Journal of Computational Chemistry published by Wiley Periodicals, Inc.