Accelerated Modeling of Lithium Diffusion in Solid State Electrolytes Using Artificial Neural Networks

Abstract

There is great interest in solid state lithium electrolytes to replace the flammable organic electrolyte for an all solid state battery. Previous efforts trying to understand the structure-function relationships resulting in high ionic conductivity materials have mainly relied on ab initio molecular dynamics. Such simulations, however, are computationally demanding and cannot be reasonably applied to large systems containing more than a few hundred atoms. Herein, we investigate using artificial neural networks (ANN) to accelerate the calculation of high accuracy atomic forces and energies used during molecular dynamics (MD) simulations (Figure 1A), to eliminate the need for costly ab initio force and energy evaluation methods, such as density functional theory (DFT). After carefully training a robust ANN for four and five element systems, we obtain nearly identical lithium ion diffusivities for Li10GeP2S12 (LGPS) when benchmarking the ANN-MD results with DFT-MD. We find that the ANN generated potentials scale linearly with atomic system size, compared to cubically in DFT (Figure 1B). To demonstrate the power of the outlined ANN-MD approach we apply it to a doped LGPS system to calculate the effect of concentrations of chlorine on the lithium diffusivity at a resolution that would be unrealistic to model with DFT-MD. We find that there is an optimal chlorine doping concentration which is a result of competing electronic effects of the effective dielectric constant and effective ionic radius of chlorine. Such effects could not be adequately studied with classical potentials. We find that ANN-MD simulations can provide the framework to study systems that require a large number of atoms more efficiently while maintaining high accuracy. Figure 1