Machine Learning Enabled Graph Analysis of Particulate Composites: Application to Solid-state Battery Cathodes

TL;DR

This paper introduces a machine learning framework that converts multimodal X-ray images of particulate composites into graphs, enabling detailed microstructure analysis and insights for solid-state battery cathodes.

Contribution

The novel ML-enabled graph analysis method automates microstructure representation from experimental images, linking microstructure features to electrochemical performance.

Findings

Identifies the importance of triple phase junctions in electrochemical activity.

Corroborates the role of ion/electron conduction channels in battery performance.

Demonstrates the effectiveness of graph-based microstructure analysis.

Abstract

Particulate composites underpin many solid-state chemical and electrochemical systems, where microstructural features such as multiphase boundaries and inter-particle connections strongly influence system performance. Advances in X-ray microscopy enable capturing large-scale, multimodal images of these complex microstructures with an unprecedentedly high throughput. However, harnessing these datasets to discover new physical insights and guide microstructure optimization remains a major challenge. Here, we develop a machine learning (ML) enabled framework that enables automated transformation of experimental multimodal X-ray images of multiphase particulate composites into scalable, topology-aware graphs for extracting physical insights and establishing local microstructure-property relationships at both the particle and network level. Using the multiphase particulate cathode of solid-state lithium batteries as an example, our ML-enabled graph analysis corroborates the critical role of triple phase junctions and concurrent ion/electron conduction channels in realizing desirable local electrochemical activity. Our work establishes graph-based microstructure representation as a powerful paradigm for bridging multimodal experimental imaging and functional understanding, and facilitating microstructure-aware data-driven materials design in a broad range of particulate composites.