ReaxFF Parameter Optimization with Monte-Carlo and Evolutionary Algorithms: Guidelines and Insights-Reference-Cited by-同舟云学术

ReaxFF Parameter Optimization with Monte-Carlo and Evolutionary Algorithms: Guidelines and Insights

Published:2019-10-27 Issue:12 Volume:15 Page:6799-6812
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Shchygol Ganna¹²,Yakovlev Alexei²,Trnka Tomáš²^ORCID,van Duin Adri C. T.³^ORCID,Verstraelen Toon¹^ORCID

Affiliation:

1. Center for Molecular Modeling (CMM), Ghent University, Technologiepark-Zwijnaarde 46, B-9052 Ghent, East Flanders, Belgium

2. Software for Chemistry & Materials (SCM) B.V., De Boelelaan 1083, 1081 HV Amsterdam, North Holland, The Netherlands

3. Department of Mechanical Engineering, The Pennsylvania State University, University Park, State College, Pennsylvania 16802, United States

Funder

H2020 Marie Sklodowska-Curie Actions

Fonds Wetenschappelijk Onderzoek

Research Board of Ghent University

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry,Computer Science Applications

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.9b00769

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