A high-dimensional neural network potential for molecular dynamics simulations of condensed phase nickel and phase transitions. Issue 3 (11th February 2023)

• Record Type: Journal Article
• Authors: Deng, Hao; Comer, Jeffrey; Liu, Bin
• Is Part Of: Molecular simulation. Volume 49:Issue 3(2023)
• Page Start: 263
• Page End: 270
• Publication Date: 2023-02-11
• Subjects: Machine learning -- molecular dynamics -- nickel phase transition -- neural network potential -- radial distribution function; Molecular dynamics -- Computer simulation -- Periodicals; Statistical mechanics -- Computer simulation -- Periodicals; 539.6
• DOI: 10.1080/08927022.2022.2156561
• Languages: English
• ISSNs: 0892-7022

Abstract

ABSTRACT: A high-dimensional neural network interatomic potential was developed and used in molecular dynamics simulations of condensed phase Ni and Ni systems with liquid–solid phase coexistence. The reference data set was generated by sampling the potential energy surface over a broad temperature-pressure domain using ab initio MD simulations to train a unified potential. Excellent agreement was achieved between bulk face-centred cubic nickel thermal expansion simulations and relevant experimental data. The same potential also yields accurate structures and diffusivities in the liquid state. The phase transition between liquid and solid phases was simulated using the two-phase interface method. The predicted melting point temperature is within a few kelvins of the literature value. The general methodology could be applied to describe crystals with much more complex phase behaviours.