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Development of a new parameter optimization scheme for a reactive force field based on a machine learning approach
Author(s) -
Nakata Hiroya,
Bai Shandan
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.25841
Subject(s) - reaxff , force field (fiction) , molecular dynamics , chemical vapor deposition , computer science , materials science , chemistry , computational chemistry , nanotechnology , artificial intelligence , interatomic potential
Reactive molecular dynamics (MD) simulation is performed using a reactive force field (ReaxFF). To this end, we developed a new method to optimize the ReaxFF parameters based on a machine learning approach. This approach combines the k ‐nearest neighbor and random forest regressor algorithm to efficiently locate several possible ReaxFF parameter sets. As a pilot test of the developed approach, the optimized ReaxFF parameter set was applied to perform chemical vapor deposition (CVD) of an α ‐Al 2 O 3 crystal. The crystal structure of α ‐Al 2 O 3 was reasonably reproduced even at a relatively high temperature (2000 K). The reactive MD simulation suggests that the (11 2 ¯ 0) surface grows faster than the (0001) surface, indicating that the developed parameter optimization technique could be used for understanding the chemical reaction in the CVD process. © 2019 Wiley Periodicals, Inc.