Development of a new parameter optimization scheme for a reactive force field based on a machine learning approach
© 2019 Wiley Periodicals, Inc.
| Veröffentlicht in: | Journal of computational chemistry. - 1984. - 40(2019), 23 vom: 05. Sept., Seite 2000-2012 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2019
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| Zugriff auf das übergeordnete Werk: | Journal of computational chemistry |
| Schlagworte: | Journal Article chemical vapor deposition machine learning reactive molecular dynamics |
| Zusammenfassung: | © 2019 Wiley Periodicals, Inc. 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-Al2 O3 crystal. The crystal structure of α-Al2 O3 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 |
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| Beschreibung: | Date Revised 23.07.2019 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1096-987X |
| DOI: | 10.1002/jcc.25841 |