Deep Potential generation scheme and simulation protocol for the Li10GeP2S12-type superionic conductors

TL;DR

This paper introduces an automated deep learning-based method to generate interatomic potentials, enabling efficient and accurate simulation of Li-ion diffusion in solid electrolytes, which was previously limited by computational costs.

Contribution

The study develops a Deep Potential Generator scheme and a simulation protocol for LiGePS-type electrolytes, significantly improving simulation efficiency while maintaining accuracy.

Findings

Diffusivity data matches experimental results

Protocol accurately computes Li-ion diffusion parameters

Automated workflow accelerates material screening

Abstract

It has been a challenge to accurately simulate Li-ion diffusion processes in battery materials at room temperature using {\it ab initio} molecular dynamics (AIMD) due to its high computational cost. This situation has changed drastically in recent years due to the advances in machine learning-based interatomic potentials. Here we implement the Deep Potential Generator scheme to \textit{automatically} generate interatomic potentials for LiGePS-type solid-state electrolyte materials. This increases our ability to simulate such materials by several orders of magnitude without sacrificing {\it ab initio} accuracy. Important technical aspects like the statistical error and size effects are carefully investigated. We further establish a reliable protocol for accurate computation of Li-ion diffusion processes at experimental conditions, by investigating important technical aspects like the statistical error and size effects. Such a protocol and the automated workflow allow us to screen materials for their relevant properties with much-improved efficiency. By using the protocol and automated workflow developed here, we obtain the diffusivity data and activation energies of Li-ion diffusion that agree well with the experiment. Our work paves the way for future investigation of Li-ion diffusion mechanisms and optimization of Li-ion conductivity of solid-state electrolyte materials.