Non-Arrhenius Li-ion transport and grain-size effects in argyrodite solid electrolytes
Yongliang Ou, Lena Scholz, Sanath Keshav, Yuji Ikeda, Marvin Kraft, Sergiy Divinski, Rafael G\'omez-Bombarelli, Wolfgang G. Zeier, Felix Fritzen, and Blazej Grabowski

TL;DR
This study combines machine learning and multiscale modeling to understand Li$^+$ transport in argyrodite solid electrolytes, revealing how microstructure and grain size influence ionic conductivity and transport mechanisms.
Contribution
It introduces a novel multiscale modeling framework with machine-learning potentials to analyze Li$^+$ transport in argyrodite electrolytes, accounting for grain boundary effects and microstructure.
Findings
Grain boundaries can enhance or suppress Li$^+$ diffusion depending on the bulk phase.
Nanosizing can activate ionic transport in non-superionic electrolytes.
Grain refinement improves intergranular contacts without reducing superionic conductivity.
Abstract
Argyrodite solid electrolytes, such as LiPSCl, exhibit some of the highest known superionic conductivities. Yet, the mechanistic understanding of Li transport in realistic argyrodite microstructures -- where atomic-scale mechanisms interplay with continuum-scale dynamics at grain boundaries -- remains limited. Here, we resolve Li transport in silico by developing accurate machine-learning potentials via closed-loop active learning and embedding the potentials in a multiscale modeling framework that integrates molecular dynamics with finite element simulations. We show that bulk diffusion barriers scale linearly with anion radius. Grain boundaries have opposite effects depending on the bulk -- enhancing Li diffusion in low-diffusivity phases but suppressing it in fast-diffusing ones. Simulations of polycrystalline LiPSI reveal non-Arrhenius transport behaviors…
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Taxonomy
TopicsAdvanced Battery Materials and Technologies · Machine Learning in Materials Science · Thermal Expansion and Ionic Conductivity
