Deciphering How Anion Clusters Govern Lithium Conduction in Glassy Thiophosphate Electrolytes through Machine Learning AITranslate

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DOI：https://doi.org/10.1021/acsenergylett.3c00237

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Glasses such as lithium thiophosphates (Li2S-P2S5) show promise as solid electrolytes for batteries, but a poor understanding of how the disordered structure affects lithium transport properties limits the development of glassy electrolytes. To address this, we here simulate glassy Li2S-P2S5 electrolytes with varying fractions of polyatomic anion clusters, i.e., P2S64–, P2S74–, and PS43–, using classical molecular dynamics. Based on the determined variation in ionic conductivity, we use a classification-based machine-learning metric termed “softness”─a structural fingerprint that is correlated to the atomic rearrangement probability─to unveil the structural origin of lithium-ion mobility. The softness distribution of lithium ions is highly spatially correlated: that is, the “soft” (high mobility) lithium ions are predominantly found around PS43– units, while the “hard” (low mobility) ions are found around P2S64– units. We also show that soft lithium-ion migration requires a smaller energy barrier to be overcome relative to that observed for hard lithium-ion migration.