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

dc.contributor.author Zhang, Chao
dc.contributor.author Sun, Yang
dc.contributor.author Wang, Hai-Di
dc.contributor.author Zhang, Feng
dc.contributor.author Wen, Tong-Qi
dc.contributor.author Ho, Kai-Ming
dc.contributor.author Wang, Cai-Zhuang
dc.contributor.department Ames Laboratory
dc.contributor.department Ames National Laboratory
dc.date 2021-02-25T23:59:08.000
dc.date.accessioned 2021-04-29T23:39:09Z
dc.date.available 2021-04-29T23:39:09Z
dc.date.embargo 2022-01-28
dc.date.issued 2021-01-28
dc.description.abstract <p>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.</p>
dc.identifier archive/lib.dr.iastate.edu/ameslab_manuscripts/836/
dc.identifier.articleid 1841
dc.identifier.contextkey 21732543
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath ameslab_manuscripts/836
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/104478
dc.language.iso en
dc.relation.ispartofseries IS-J 10415
dc.source.bitstream archive/lib.dr.iastate.edu/ameslab_manuscripts/836/IS_J_10415.pdf|||Sat Jan 15 02:10:17 UTC 2022
dc.source.uri 10.1021/acs.jpcc.0c08873
dc.subject.disciplines Physical Chemistry
dc.subject.disciplines Physics
dc.subject.keywords phase separation
dc.subject.keywords crystallization process
dc.subject.keywords phosphorus-tin system
dc.subject.keywords molecular dynamics simulation
dc.subject.keywords neural network potential
dc.title Crystallization of the P3Sn4 Phase upon Cooling P2Sn5 Liquid by Molecular Dynamics Simulation Using a Machine Learning Interatomic Potential
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isOrgUnitOfPublication 25913818-6714-4be5-89a6-f70c8facdf7e
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