Tackling Structural Complexity in Li₂S-P₂S₅ Solid-State Electrolytes Using Machine Learning Potentials.

ABSTRACT: The lithium thiophosphate (LPS) material class provides promising candidates for solid-state electrolytes (SSEs) in lithium ion batteries due to high lithium ion conductivities, non-critical elements, and low material cost. LPS materials are characterized by complex thiophosphate microchemistry and structural disorder influencing the material performance. To overcome the length and time scale restrictions of ab initio calculations to industrially applicable LPS materials, we develop a near-universal machine-learning interatomic potential for the LPS material class. The trained Gaussian Approximation Potential (GAP) can likewise describe crystal and glassy materials and different P-S connectivities PmSn. We apply the GAP surrogate model to probe lithium ion conductivity and the influ

SUBMITTER: Staacke CG 

PROVIDER: S-EPMC9458117 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature