Thermal transport of Li₃PS₄ solid electrolytes with ab initio accuracy

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 work, we leverage recently developed machine-learning potentials targeting different ab initio functionals (PBEsol, r2SCAN, PBE0) and a state-of-the-art formulation of the Green-Kubo theory of heat transport in multicomponent systems to compute the thermal conductivity of a promising solid-state-electrolyte, Li3PS4, in all its polymorphs (α, β and γ). By comparing MD estimates with lattice methods on the low-temperature, non-diffusive γ-Li3PS4, we highlight strong anharmonicities and negligible nuclear quantum effects, hence further justifying MD-based methods even for non-diffusive phases. Finally, for the ion-conducting α and β phases, where the multicomponent Green-Kubo MD approach is mandatory, our simulations indicate a weak temperature dependence of the thermal conductivity, a glass-like behavior due to the effective local disorder characterizing these Li-diffusing phases.

搜集汇总

背景与挑战

背景概述

该数据集聚焦于Li₃PS₄固体电解质的热传导性能研究，通过机器学习势能和格林-库博理论方法，计算了其所有多晶型（α、β和γ）的热导率。研究发现，离子导电的α和β相表现出弱温度依赖性和类似玻璃的行为，这归因于锂扩散相的有效局部无序性。数据集包含计算数据、相关文件以及参考文献，旨在支持固体电解质热管理领域的进一步分析。