Exploring Li-Ion Transport Properties of Li3TiCl6: A Machine Learning Molecular Dynamics Study

Selva Chandrasekaran Selvaraj

Abstract:

We performed large-scale molecular dynamics simulations based on a machine-learning force field (MLFF) to investigate the Liion transport mechanism in cation-disordered Li3TiCl6 cathode at six different temperatures, ranging from 25°C to 100°C. In this
work, deep neural network method and data generated by ab − initio molecular dynamics (AIMD) simulations were deployed to
build a high-fidelity MLFF. Radial distribution functions, Li-ion mean square displacements (MSD), diffusion coefficients, ionic
conductivity, activation energy, and crystallographic direction-dependent migration barriers were calculated and compared with
corresponding AIMD and experimental data to benchmark the accuracy of the MLFF. From MSD analysis, we captured both the
self and distinct parts of Li-ion dynamics. The latter reveals that the Li-ions are involved in anti-correlation motion that was rarely
reported for solid-state materials. Similarly, the self and distinct parts of Li-ion dynamics were used to determine Haven’s ratio to
describe the Li-ion transport mechanism in Li3TiCl6. Obtained trajectory from molecular dynamics infers that the Li-ion
transportation is mainly through interstitial hopping which was confirmed by intra- and inter-layer Li-ion displacement with respect
to simulation time. Ionic conductivity (1.06 mS/cm) and activation energy (0.29eV) calculated by our simulation are highly
comparable with that of experimental values. Overall, the combination of machine-learning methods and AIMD simulations
explains the intricate electrochemical properties of the Li3TiCl6 cathode with remarkably reduced computational time. Thus, our
work strongly suggests that the deep neural network-based MLFF could be a promising method for large-scale complex materials.