Regensburg 2025 – scientific programme
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MM: Fachverband Metall- und Materialphysik
MM 32: Transport in Materials: Diffusion, Charge or Heat Conduction
MM 32.2: Talk
Thursday, March 20, 2025, 15:15–15:30, H22
Crossing Boundaries? Probing Ion Conduction across Interfaces in Solid Electrolytes using Computational NMR Spectroscopy — •Tabea Huss1, Federico Civaia1, Simone Köcher2,1, Karsten Reuter1, and Christoph Scheurer1,2 — 1Fritz-Haber-Institut der MPG, Berlin — 2Institute of Energy Technologies (IET-1), Forschungszentrum Jülich GmbH
Grain boundaries are critical, yet poorly understood factors affecting ion transport in solid-state electrolytes. The spin-alignment echo (SAE) nuclear magnetic resonance (NMR) experiment is a versatile tool to study the manifold transport processes of quadrupolar ions in these solid state materials. However, assigning the measured decay coefficients to physical transport phenomena often proves to be challenging. We have previously demonstrated that we can replicate the SAE experiment for bulk materials using a multi-scale machine learning framework.[1] This framework simulates both the atomic structure and dynamics of solid-state systems, along with generating solid state NMR observables. Our approach has already allowed us to predict electric field gradients over molecular dynamics trajectories and use them to compute decay constants that align with ion hopping times in bulk lithium thiophosphates. In this work, we extend our methodology to explore ion transport in grain boundary structures of the solid-state electrolyte Li10GeP2S12. We extract SAE time constants and differentiate among various decay processes, advancing another step towards direct comparability with experimental results.
[1] A. F. Harper et al., Faraday Discuss., (2024).
Keywords: Solid State Electrolytes; Ion Transport; Batteries; Machine Learning; NMR