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Liquid to crystal Si growth simulation using machine learning force field

Journal Article · · Journal of Chemical Physics
DOI:https://doi.org/10.1063/5.0011163· OSTI ID:1829057
 [1];  [2]
  1. Huazhong Univ. of Science and Technology, Wuhan (China)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
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Machine learning force field (ML-FF) has emerged as a potential promising approach to simulate various material phenomena for large systems with ab initio accuracy. However, most ML-FFs have been used to study the phenomena relatively close to the equilibrium ground states. In this work, we have studied a far from equilibrium system of liquid to crystal Si growth using ML-FF. Here, we found that our ML-FF based on ab initio decomposed atomic energy can reproduce all the aspects of ab initio simulated growth, from local energy fluctuations to transition temperatures, to diffusion constant, and growth rates. We have also compared the growth simulation with the Stillinger-Weber classical force field and found significant differences. A procedure is also provided to correct a systematic fitting bias in the ML-FF training process, which exists in all training models, otherwise critical results like transition temperature will be wrong.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division
Grant/Contract Number:
AC02-05CH11231
OSTI ID:
1829057
Alternate ID(s):
OSTI ID: 1647680
Journal Information:
Journal of Chemical Physics, Journal Name: Journal of Chemical Physics Journal Issue: 7 Vol. 153; ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)Copyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Atomic energy
Atomic structure
Classical force fields
Crystallography
Density functional theory
Machine learning
Molecular dynamics
Phase transitions