Lithium Ion Conduction in Cathode Coating Materials from On-the-Fly Machine Learning

Abstract

The performance of solid-state lithium ion batteries can be improved through the use of interfacial coating materials, but computationally identifying materials with sufficiently high lithium-ion conductivity can be challenging. Methods such as ab initio molecular dynamics that work well for superionic conductors can be prohibitively expensive when used on materials that conduct lithium ions less well but are still suitable for use as interfacial coatings. We demonstrate a way to address this problem using machine-learned interatomic potentials models in the form of moment tensor potentials. To prevent the potentials from significantly deviating from density functional theory calculations, we use molecular dynamics simulations coupled with on-the-fly machine learning. This approach increases the efficiency of the calculations by 7 orders of magnitude compared to purely ab initio molecular dynamics, significantly reducing the uncertainty in calculated migration energies and improving agreement with experimentally determined activation energies. Using this approach, we identify two particularly promising materials for use as coatings in batteries as well as several others that are candidates for doping-enhanced ionic conduction.