The study of the convergence conditions of the deep-learning method in the Li10GeP2S12 solid state electrolyte system

Abstract

Abstract The ionic conductivity of solid-state electrolytes at room temperature is crucial for commercializing lithium-ion batteries with solid-state electrolytes. Ab initio methods encounter a challenge due to their substantial computational resource demands. Classical molecular dynamics methods, on the other hand, are suitable for large-scale systems with simulation times reaching the nanosecond scale. However, they rely on empirical parameters in force fields, limiting their use to systems with well-established and extensively validated parameters, which is a constraint in studying new materials. A simulation approach combines ab initio simulations and classical molecular dynamics. Deepmd-kit, a deep learning tool, trains a tailored force field model using ab initio simulation data for the target system. However, as an approximate method, its reliability must be compared with ab initio results. As a data-sensitive method, the amount of data required to achieve the desired accuracy varies for different systems and must be tested accordingly. This paper performs convergence training for system size and simulation time concerning Li10GeP2S12 solid-state electrolytes, establishing the convergence criteria for deep learning data in this system.