Turning Insulators into Accelerators: Deciphering the Interfacial Conductivity Boost in ZrO2-Li2ZrCl6 Composites through Machine Learning Molecular Dynamics Simulations

TL;DR

This study uses machine learning and molecular dynamics to uncover how ZrO2 enhances ionic conductivity at interfaces with Li2ZrCl6 in solid-state batteries, revealing amorphization and Li+ trapping as key factors.

Contribution

Developed a machine-learned force field enabling large-scale simulations to elucidate interfacial mechanisms boosting conductivity in ZrO2-Li2ZrCl6 composites.

Findings

Interfacial amorphization driven by space-charge effects enhances Li+ mobility.

Distorted amorphous regions trap Li+ and increase hopping activity.

Surface charge redistribution affects local Li+ availability.

Abstract

Halide solid-state electrolytes have emerged as promising candidates for all-solid-state lithium batteries due to their high oxidative stability and deformability, yet their moderate ionic conductivity remains a bottleneck. While incorporating ionically insulating ZrO2 nanoparticles (Nat. Commun. 2023, 14, 2459) has been experimentally shown to enhance the ionic conductivity of Li2ZrCl6, the atomistic origin governing this interfacial phenomenon remains unclear. Here, we bridge the spatiotemporal gap in modeling complex heterostructures by developing an accurate machine-learned force fields based on neuroevolution potential, enabling large-scale molecular dynamics simulations of ZrO2/Li2ZrCl6 heterostructures. By systematically investigating four representative low-lattice-mismatch ZrO2/Li2ZrCl6 interfaces, we identify spontaneous interfacial amorphization driven by space-charge effects upon surface cleavage, trapping Li+ and leading to under-coordinated Li+ polyhedrons with pronounced geometric distortion. These distorted amorphous interfacial regions exhibit markedly enhanced Li+ hopping activity, significantly outperforming the bulk lattice, provided that local mobile Li+ inventory is not depleted by surface charge redistribution. This work establishes a computational framework for training validated machine-learned force fields for interfaces and provides mechanistic understandings of the interfacial conductivity boost in the insulator-conductor composites, guiding the rational design of electrolytes toward next-generation solid-state batteries.