Revealing Fast Ionic Conduction in Solid Electrolytes through Machine Learning Accelerated Raman Calculations

TL;DR

This paper introduces a machine learning-accelerated method to identify solid electrolytes with fast ionic conduction by analyzing low-frequency Raman spectra, bridging simulations and experiments for accelerated discovery.

Contribution

We develop a novel computational pipeline combining machine learning and Raman calculations to detect liquid-like ion conduction signatures in solid electrolytes.

Findings

Successfully identified Raman signatures of fast ionic conduction in sodium-ion conductors.

Achieved near-ab initio accuracy in Raman spectrum predictions for disordered materials.

Demonstrated the method's predictive power for discovering promising solid electrolytes.

Abstract

Fast ionic conduction is a defining property of solid electrolytes for all-solid-state batteries. Previous studies have suggested that liquid-like cation motion associated with fast ionic transport can disrupt crystalline symmetry, thereby lifting Raman selection rules. Here, we exploit the resulting low-frequency, diffusive Raman scattering as a spectral signature of fast ionic conduction and develop a machine learning-accelerated computational pipeline to identify promising solid electrolytes based on this feature. By overcoming the steep computational barriers to calculating Raman spectra of strongly disordered materials at finite temperatures, we achieve near-ab initio accuracy and demonstrate the predictive power of our approach for sodium-ion conductors, revealing clear Raman signatures of liquid-like ion conduction. This work highlights how machine learning can bridge atomistic simulations and experimental observables, enabling data-efficient discovery of fast-ion conductors.