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231225s2019 xx |||||o 00| ||eng c |
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|a 10.1002/jcc.25841
|2 doi
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| 041 |
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|a eng
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| 100 |
1 |
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|a Nakata, Hiroya
|e verfasserin
|4 aut
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| 245 |
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|a Development of a new parameter optimization scheme for a reactive force field based on a machine learning approach
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|c 2019
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
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|a Date Revised 23.07.2019
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2019 Wiley Periodicals, Inc.
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|a 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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|a Journal Article
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|a chemical vapor deposition
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| 650 |
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|a machine learning
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| 650 |
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4 |
|a reactive molecular dynamics
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| 700 |
1 |
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|a Bai, Shandan
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 40(2019), 23 vom: 05. Sept., Seite 2000-2012
|w (DE-627)NLM098138448
|x 1096-987X
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|g volume:40
|g year:2019
|g number:23
|g day:05
|g month:09
|g pages:2000-2012
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|u http://dx.doi.org/10.1002/jcc.25841
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