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EZFF: Python library for multi-objective parameterization and uncertainty quantification of interatomic forcefields for molecular dynamics

Journal Article · · SoftwareX
 [1];  [2];  [3];  [4];  [2];  [2];  [2];  [2];  [3];  [3]
  1. Univ. of Southern California, Los Angeles, CA (United States); Univ. of Southern California, Los Angeles, CA (United States)
  2. Univ. of Southern California, Los Angeles, CA (United States)
  3. Georgia Institute of Technology, Atlanta, GA (United States)
  4. Univ. of Southern California, Los Angeles, CA (United States); California State University, Bakersfield, CA (United States)
+ Show Author Affiliations

Parameterization of interatomic forcefields is a necessary first step in performing molecular dynamics simulations. This is a non-trivial global optimization problem involving quantification of multiple empirical variables against one or more properties. We present EZFF, a lightweight Python library for parameterization of several types of interatomic forcefields implemented in several molecular dynamics engines against multiple objectives using genetic-algorithm-based global optimization methods. The EZFF scheme provides unique functionality such as the parameterization of hybrid forcefields composed of multiple forcefield interactions as well as built-in quantification of uncertainty in forcefield parameters and can be easily extended to other forcefield functional forms as well as MD engines.

Research Organization:
Univ. of Southern California, Los Angeles, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC); Office of Naval Research (ONR); National Science Foundation (NSF)
Grant/Contract Number:
SC0014607
OSTI ID:
1851957
Journal Information:
SoftwareX, Journal Name: SoftwareX Journal Issue: C Vol. 13; ISSN 2352-7110
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

97 MATHEMATICS AND COMPUTING
genetic algorithm
global optimization
interatomic forcefield
molecular dynamics