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MM: Fachverband Metall- und Materialphysik
MM 12: Materials for the Storage and Conversion of Energy III / Functional Materials I
MM 12.6: Vortrag
Dienstag, 10. März 2026, 11:45–12:00, SCH/A216
Ion Transport in Mixed-Halide Lithium Argyrodites from Machine Learning Potentials — •Yufeng Xu, Takeru Miyagawa, Waldemar Kaiser, and David A. Egger — Physics Department, TUM School of Natural Sciences, Technical University of Munich, 85748 Garching, Germany
Lithium argyrodites Li6PS5X (X=Cl, Br, I) are structurally and chemically complex solid electrolytes. Their ionic conductivity is strongly affected by static forms of disorder, including sulfur-halide anti-site defects [1] and compositional disorder introduced through halide mixing [2]. In this study, we use the MACE message-passing architecture [3] together with machine-learning molecular dynamics to examine ion transport mechanisms in both pure and mixed-halide Li6PS5X. We analyze how static and dynamic disorder in mixed-halide systems shapes Li ion diffusion, and how these changes in ion conduction are reflected in the vibrational spectra of the argyrodites. References: [1] B.J. Morgan, Chem. Mater., 2021, 33, 6, 2004-2018. [2] S.V. Patel et al. Chem. Mater., 2021, 33, 4, 1435-1443. [3] I. Batatia et al. J. Chem. Phys., 2025, 163, 184110.
Keywords: Solid electrolytes; Machine learning potentials; Ion transport; Lithium argyrodites