Machine Learning Enabled Graph Analysis of Particulate Composites: Application to Solid-state Battery Cathodes
This work addresses the problem of microstructure optimization for materials scientists and engineers in fields like solid-state batteries, offering a new paradigm for data-driven design, though it is incremental in applying graph-based methods to a specific domain.
The researchers tackled the challenge of analyzing large-scale multimodal X-ray images of particulate composites by developing a machine learning framework that transforms these images into scalable graphs, enabling the discovery of physical insights such as the critical role of triple phase junctions and ion/electron conduction channels in solid-state battery cathodes.
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.