Journal
JOURNAL OF COMPUTATIONAL CHEMISTRY
Volume 40, Issue 23, Pages 2000-2012Publisher
WILEY
DOI: 10.1002/jcc.25841
Keywords
machine learning; reactive molecular dynamics; chemical vapor deposition
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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 alpha-Al2O3 crystal. The crystal structure of alpha-Al2O3 was reasonably reproduced even at a relatively high temperature (2000 K). The reactive MD simulation suggests that the (112 over bar 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. (c) 2019 Wiley Periodicals, Inc.
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