Publication Date

1-28-2021

Department

Ames Laboratory; Physics and Astronomy

Campus Units

Ames Laboratory, Physics and Astronomy

OSTI ID+

1765704

Report Number

IS-J 10415

DOI

10.1021/acs.jpcc.0c08873

Journal Title

The Journal of Physical Chemistry C

Volume Number

125

Issue Number

5

First Page

3127

Last Page

3133

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.

DOE Contract Number(s)

AC02-07CH11358

Language

en

Publisher

Iowa State University Digital Repository, Ames IA (United States)

Available for download on Friday, January 28, 2022

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