Simulating diffusion properties of solid-state electrolytes via a neural network potential: Performance and training scheme

TL;DR

This paper evaluates the DeePMD neural network potential for simulating ion diffusion in solid-state electrolytes, demonstrating its accuracy and proposing a training scheme for key battery-relevant properties.

Contribution

It assesses DeePMD's performance on real electrolyte systems and develops a training protocol for diffusion coefficient prediction.

Findings

DeePMD accurately models ion diffusion in electrolytes.

The proposed training scheme effectively computes diffusion coefficients.

Results align well with previous computational data.

Abstract

The recently published DeePMD model (https://github.com/deepmodeling/deepmd-kit), based on a deep neural network architecture, brings the hope of solving the time-scale issue which often prevents the application of first principle molecular dynamics to physical systems. With this contribution we assess the performance of the DeePMD potential on a real-life application and model diffusion of ions in solid-state electrolytes. We consider as test cases the well known Li10GeP2S12, Li7La3Zr2O12 and Na3Zr2Si2PO12. We develop and test a training protocol suitable for the computation of diffusion coefficients, which is one of the key properties to be optimized for battery applications, and we find good agreement with previous computations. Our results show that the DeePMD model may be a successful component of a framework to identify novel solid-state electrolytes.