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MM: Fachverband Metall- und Materialphysik

MM 12: Materials for the Storage and Conversion of Energy

MM 12.8: Vortrag

Dienstag, 18. März 2025, 12:15–12:30, H22

Ion Dynamics in Li-Garnet Electrolytes from Machine-Learning Molecular Dynamics and Raman Spectroscopy — •Takeru Miyagawa¹, Hyunwon Chu², Willis O’Leary², Manuel Grumet¹, Jennifer L.M. Rupp^1,2, Waldemar Kaiser¹, and David A. Egger¹ — ¹TUM School of Natural Sciences, Technical University of Munich — ²Department of Materials Science and Engineering, Massachusetts Institute of Technology

Lithium lanthanum zirconate (LLZO) is a promising electrolyte compound for solid-state batteries. Despite subtle differences in the structural properties, its cubic phase, often stabilized by doping, strongly exceeds the tetragonal counterpart in its ionic conductivity. Here, we study the interplay of Li ion migration and host lattice dynamics in tetragonal and cubic LLZO, and compare the ion dynamics to Ta-doped LLZO, using machine-learning molecular dynamics benchmarked in our previous study [1]. We observe a strongly correlated Li-ion migration in the undoped cubic LLZO at increased temperatures, whereas the tetragonal phase showed no Li ion conduction. In contrast, Li ion hopping is the dominant mechanism in Ta-doped cubic LLZO. Additionally, we compute finite-temperature Raman spectra [2] of the LLZO materials and correlate them to experiments. Our predicted Raman results accurately align with measured Raman spectra, allowing us to reveal concrete vibrational motifs that may be utilized to screen LLZO films for the presence of the conductive cubic phase. [1] Miyagawa, et al. J. Mater. Chem. A 12, 11344 (2024) [2] Thomas, et al. Phys. Chem. Chem. Phys. 15, 6608-6622 (2013)

Keywords: Solid-State Electrolytes; Lithium Ion Conductors; Raman Spectroscopy; Machine Learning Molecular Dynamics