Tackling Structural Complexity in Li(2)S-P(2)S(5) Solid-State Electrolytes Using Machine Learning Potentials

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 P [Formula: see text] S [Formula: see text]. We apply the GAP surrogate model to probe lithium ion conductivity and the influence of thiophosphate subunits on the latter. The materials studied are crystals (modifications of Li [Formula: see text] PS [Formula: see text] and Li [Formula: see text] P [Formula: see text] S [Formula: see text]), and glasses of the xLi [Formula: see text] S–(100 – x)P [Formula: see text] S [Formula: see text] type (x = 67, 70 and 75). The obtained material properties are well aligned with experimental findings and we underscore the role of anion dynamics on lithium ion conductivity in glassy LPS. The GAP surrogate approach allows for a variety of extensions and transferability to other SSEs.