Physics-Informed ML Exploration of Structure-Transport Relationships in Hard Carbon

TL;DR

This study develops a physics-informed, machine learning-based framework to understand how microstructural features influence sodium-ion transport in hard carbon anodes, aiding the design of better energy storage materials.

Contribution

It introduces a novel data-driven approach combining ML potentials and MD simulations to elucidate structure-transport relationships in disordered carbon.

Findings

Identified distinct sodium diffusion modes such as hopping and trapping.

Highlighted tortuosity and NaNa coordination as key transport determinants.

Connected microstructural features to processing variables like density and sodium content.

Abstract

Sodium-ion batteries are a cost-effective and sustainable alternative to lithium-ion systems for large-scale energy storage. Hard carbon (HC) anodes, composed of disordered graphitic and amorphous domains, offer high capacity but exhibit complex, poorly understood ion transport behavior. In particular, the relationship between local microstructure and sodium mobility remains unresolved, hindering rational performance optimization. Here, we introduce a data-driven framework that combines machine-learned interatomic potentials with molecular dynamics simulations to systematically investigate sodium diffusion across a broad range of carbon densities and sodium loadings. By computing per-ion structural descriptors, we identify the microscopic factors that govern ion transport. Unsupervised learning uncovers distinct diffusion modes, including hopping, clustering, and void trapping, while supervised analysis highlights tortuosity and NaNa coordination as primary determinants of mobility. Correlation mapping further connects these transport regimes to processing variables such as bulk density and sodium content. This physics-informed approach establishes quantitative structure-transport relationships that capture the heterogeneity of disordered carbon. Our findings deliver mechanistic insights into sodium-ion dynamics and provide actionable design principles for engineering high-performance HC anodes in next-generation battery systems.