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

Zhang, Chao; Sun, Yang; Wang, Hai-Di; Zhang, Feng; Wen, Tong-Qi; Ho, Kai-Ming; Wang, Cai-Zhuang

doi:10.1021/acs.jpcc.0c08873

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

Journal Article · Thu Jan 28 00:00:00 EST 2021 · Journal of Physical Chemistry. C

DOI:https://doi.org/10.1021/acs.jpcc.0c08873· OSTI ID:1765704

^[1];

^[2]; Wang, Hai-Di ^[3]; Zhang, Feng ^[4]; Wen, Tong-Qi ^[4]; Ho, Kai-Ming ^[3];

^[4]

Yantai Univ. (China); Ames Lab., Ames, IA (United States); Iowa State Univ., Ames, IA (United States)
Columbia Univ., New York, NY (United States)
Iowa State Univ., Ames, IA (United States)
Ames Lab., Ames, IA (United States); Iowa State Univ., Ames, IA (United States)

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.

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Research Organization:: Ames Lab., Ames, IA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)

Sponsoring Organization:: 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

Grant/Contract Number:: AC02-07CH11358; 11874318; ZR2018MA043; EAR-1918134; EAR-1918126

OSTI ID:: 1765704

Report Number(s):: IS-J-10,415

Journal Information:: Journal of Physical Chemistry. C, Vol. 125, Issue 5; ISSN 1932-7447

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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

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