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

2025-12-17

AI Summary

The unclear relationship between microstructural features—such as triple-phase boundaries (TPBs) and particle connectivity—and macroscopic electrochemical performance in multiphase particulate composites (e.g., cathodes of solid-state batteries) hinders rational microstructure design. Method: This study proposes a machine learning–driven, topology-aware graph analytics framework: (i) integrating deep learning–based image segmentation with multiphase boundary detection to automatically convert multimodal X-ray microtomography data into physically constrained graphs; (ii) establishing the first microstructure graph representation paradigm tailored for particulate materials. Contribution/Results: We quantitatively demonstrate, for the first time, that TPBs and percolating ion/electron conduction pathways dominantly govern local electrochemical activity. A scalable microstructure graph database is constructed, enabling precise identification of critical conduction paths and improving local activity prediction accuracy by over 40%, thereby providing a new paradigm for physics-informed microstructural optimization.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

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.

Problem

• Automates transformation of X-ray images into graphs for microstructure analysis
• Identifies key microstructural features affecting solid-state battery performance
• Bridges experimental imaging with data-driven design of particulate composites

Innovation

• Machine learning transforms X-ray images into graphs
• Graph analysis reveals triple phase junction importance
• Graph-based representation bridges imaging and materials design