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Title: Crystallization of the P3Sn4 Phase upon Cooling P2Sn5 Liquid by Molecular Dynamics Simulation Using a Machine Learning Interatomic Potential

Abstract

We performed molecular dynamics simulations to study the crystallization of the P3Sn4 phase from P2Sn5 liquid using a machine learning (ML) interatomic potential with desirable efficiency and accuracy. Our results capture the liquid properties of P2Sn5 at 1300 K, which is well above the melting temperature. The phase separation and crystallization are observed when P2Sn5 liquid is cooled down below 832 and 505 K, respectively. The simulation results are in good agreement with the experimentally observed phase transformation behaviors and provide useful insights into the complex nucleation and crystallization process at the details of atomistic scale. Our work also demonstrated that ML interatomic potentials based on neural network deep learning are robust and capable of accurately describing the energetics and kinetics of complex materials through molecular dynamics simulations.

Authors:
ORCiD logo [1]; ORCiD logo [2];  [3];  [4];  [4];  [3]; ORCiD logo [4]
  1. Yantai Univ. (China); Ames Lab., Ames, IA (United States); Iowa State Univ., Ames, IA (United States)
  2. Columbia Univ., New York, NY (United States)
  3. Iowa State Univ., Ames, IA (United States)
  4. Ames Lab., Ames, IA (United States); Iowa State Univ., Ames, IA (United States)
Publication Date:
Research Org.:
Ames Lab., Ames, IA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
Sponsoring Org.:
National Natural Science Foundation of China (NSFC); Natural Science Foundation of Shandong Province; National Science Foundation (NSF); USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division
OSTI Identifier:
1765704
Report Number(s):
IS-J-10,415
Journal ID: ISSN 1932-7447
Grant/Contract Number:  
AC02-07CH11358; 11874318; ZR2018MA043; EAR-1918134; EAR-1918126
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of Physical Chemistry. C
Additional Journal Information:
Journal Volume: 125; Journal Issue: 5; Journal ID: ISSN 1932-7447
Publisher:
American Chemical Society
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; phase separation; crystallization process; phosphorus-tin system; molecular dynamics simulation; neural network potential

Citation Formats

Zhang, Chao, Sun, Yang, Wang, Hai-Di, Zhang, Feng, Wen, Tong-Qi, Ho, Kai-Ming, and Wang, Cai-Zhuang. Crystallization of the P3Sn4 Phase upon Cooling P2Sn5 Liquid by Molecular Dynamics Simulation Using a Machine Learning Interatomic Potential. United States: N. p., 2021. Web. doi:10.1021/acs.jpcc.0c08873.
Zhang, Chao, Sun, Yang, Wang, Hai-Di, Zhang, Feng, Wen, Tong-Qi, Ho, Kai-Ming, & Wang, Cai-Zhuang. Crystallization of the P3Sn4 Phase upon Cooling P2Sn5 Liquid by Molecular Dynamics Simulation Using a Machine Learning Interatomic Potential. United States. https://doi.org/10.1021/acs.jpcc.0c08873
Zhang, Chao, Sun, Yang, Wang, Hai-Di, Zhang, Feng, Wen, Tong-Qi, Ho, Kai-Ming, and Wang, Cai-Zhuang. 2021. "Crystallization of the P3Sn4 Phase upon Cooling P2Sn5 Liquid by Molecular Dynamics Simulation Using a Machine Learning Interatomic Potential". United States. https://doi.org/10.1021/acs.jpcc.0c08873. https://www.osti.gov/servlets/purl/1765704.
@article{osti_1765704,
title = {Crystallization of the P3Sn4 Phase upon Cooling P2Sn5 Liquid by Molecular Dynamics Simulation Using a Machine Learning Interatomic Potential},
author = {Zhang, Chao and Sun, Yang and Wang, Hai-Di and Zhang, Feng and Wen, Tong-Qi and Ho, Kai-Ming and Wang, Cai-Zhuang},
abstractNote = {We performed molecular dynamics simulations to study the crystallization of the P3Sn4 phase from P2Sn5 liquid using a machine learning (ML) interatomic potential with desirable efficiency and accuracy. Our results capture the liquid properties of P2Sn5 at 1300 K, which is well above the melting temperature. The phase separation and crystallization are observed when P2Sn5 liquid is cooled down below 832 and 505 K, respectively. The simulation results are in good agreement with the experimentally observed phase transformation behaviors and provide useful insights into the complex nucleation and crystallization process at the details of atomistic scale. Our work also demonstrated that ML interatomic potentials based on neural network deep learning are robust and capable of accurately describing the energetics and kinetics of complex materials through molecular dynamics simulations.},
doi = {10.1021/acs.jpcc.0c08873},
url = {https://www.osti.gov/biblio/1765704}, journal = {Journal of Physical Chemistry. C},
issn = {1932-7447},
number = 5,
volume = 125,
place = {United States},
year = {Thu Jan 28 00:00:00 EST 2021},
month = {Thu Jan 28 00:00:00 EST 2021}
}

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