Constructing accurate machine-learned potentials and performing highly efficient atomistic simulations to predict structural and thermal properties

TL;DR

This paper develops a neural network-based potential trained on ab initio data to enable fast, accurate atomistic simulations of Cu7PS6, revealing insights into its structure, dynamics, and potential for thermoelectric applications.

Contribution

It introduces a neuroevolution potential trained on AIMD data, achieving high accuracy and significantly increased computational speed for simulating Cu7PS6.

Findings

NEP achieves 41-fold faster simulations than MTP.

Both potentials accurately reproduce phonon DOS and RDF.

Insights into Cu-ion diffusion dynamics in Cu7PS6.

Abstract

The $\text{Cu}_7\text{P}\text{S}_6$ compound has garnered significant attention due to its potential in thermoelectric applications. In this study, we introduce a neuroevolution potential (NEP), trained on a dataset generated from ab initio molecular dynamics (AIMD) simulations, using the moment tensor potential (MTP) as a reference. The low root mean square errors (RMSEs) for total energy and atomic forces demonstrate the high accuracy and transferability of both the MTP and NEP. We further calculate the phonon density of states (DOS) and radial distribution function (RDF) using both machine learning potentials, comparing the results to density functional theory (DFT) calculations. While the MTP potential offers slightly higher accuracy, the NEP achieves a remarkable 41-fold increase in computational speed. These findings provide detailed microscopic insights into the dynamics and rapid Cu-ion diffusion, paving the way for future studies on Cu-based solid electrolytes and their applications in energy devices.