Lorenzo Gigli, Davide Tisi, Federico Grasselli, Michele Ceriotti
Lithium ortho-thiophosphate (Li3 PS4 ) has emerged as a promising candidate for solid-state electrolyte batteries, thanks to its highly conductive phases, cheap components, and large electrochemical stability range. Nonetheless, the microscopic mechanisms of Li-ion transport in Li3 PS4 are far from being fully understood, the role of PS4 dynamics in charge transport still being controversial. In this work, we build machine learning potentials targeting state-of-the-art DFT references (PBEsol, r2 SCAN, and PBE0) to tackle this problem in all known phases of Li3 PS4 (α, β, and γ), for large system sizes and time scales...
February 13, 2024: Chemistry of Materials: a Publication of the American Chemical Society