Thermal conductivity of Li$_3$PS$_4$ solid electrolytes with ab initio accuracy
Davide Tisi, Federico Grasselli, Lorenzo Gigli, Michele Ceriotti

TL;DR
This study uses machine-learning potentials and Green-Kubo theory to accurately compute the thermal conductivity of Li3PS4 solid electrolytes across different phases, revealing weak temperature dependence and anharmonic effects.
Contribution
It introduces a combined machine-learning and Green-Kubo approach for ab initio accuracy thermal conductivity calculations in complex solid electrolytes.
Findings
Weak temperature dependence of thermal conductivity in Li3PS4 phases.
Strong anharmonicities and negligible nuclear quantum effects in the gamma phase.
Validation of MD methods for nondiffusive phases.
Abstract
The vast amount of computational studies on electrical conduction in solid-state electrolytes is not mirrored by comparable efforts addressing thermal conduction, which has been scarcely investigated despite its relevance to thermal management and (over)heating of batteries. The reason for this lies in the complexity of the calculations: on one hand, the diffusion of ionic charge carriers makes lattice methods formally unsuitable, due to the lack of equilibrium atomic positions needed for normal-mode expansion. On the other hand, the prohibitive cost of large-scale molecular dynamics (MD) simulations of heat transport in large systems at ab initio levels has hindered the use of MD-based methods. In this paper, we leverage recently developed machine-learning potentials targeting different ab initio functionals (PBEsol, rSCAN, PBE0) and a state-of-the-art formulation of the Green-Kubo…
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Taxonomy
TopicsMachine Learning in Materials Science · Fuel Cells and Related Materials · Advanced Battery Materials and Technologies
