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MM: Fachverband Metall- und Materialphysik
MM 12: Materials for the Storage and Conversion of Energy
MM 12.7: Vortrag
Dienstag, 18. März 2025, 12:00–12:15, H22
Rapid Identification of Ion Migration in Solid-State Ion Conductors from Machine-Learning Raman Spectroscopy — Manuel Grumet1, Takeru Miyagawa1, Karin S. Thalmann2, Tomáš Bučko3,4, •Waldemar Kaiser1, and David A. Egger1 — 1TUM School of Natural Sciences, Technical University of Munich — 2Institute of Physics, University of Freiburg — 3Faculty of Natural Sciences, Comenius University of Bratislava — 4Institute of Inorganic Chemistry, Slovak Academy of Sciences
Raman spectroscopy is a rapid, non-invasive, and widely available technique that provides a fingerprint of atomic vibrations within solid-state materials. In this work, we demonstrate evidence of Raman signatures that arise from the migration of ions within solid-state ion conductors. We use a rapid computational framework, which consists of machine-learning molecular dynamics simulations [1] and machine-learned polarizability tensors [2], to predict finite-temperature Raman spectra of two classes of superionic conductors, i.e. AgI [3] and Na3PnS4 (Pn=P,Sb) [4]. Our simulation results indicate pronounced and broad low-energy Raman intensities due to the host lattice that are correlated with the diffusion of cations. These insights can open novel synergies with experiments to rapidly screen novel compounds for future battery materials. [1] Miyagawa, et al. J. Mater. Chem. A. 12, 11344-11361 (2024) [2] Grumet, et al. J. Phys. Chem. C, 128, 15, 6464-6470 (2024) [3] Brenner, et al. Phys. Rev. Mater. 4, 115402 (2020) [4] Brenner, et al. J. Phys. Chem. Lett. 13, 25, 5938-5945 (2022)
Keywords: Solid-State Ion Conductors; Machine Learning; Raman Spectroscopy