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

Area of Science:

• Materials Science
• Data Science
• Electrochemistry

Background:

• Particulate composites are crucial for solid-state electrochemical systems.
• Microstructural features significantly impact composite performance.
• High-throughput X-ray microscopy generates complex datasets.

Purpose of the Study:

• Develop a machine learning framework for analyzing multimodal X-ray images.
• Enable automated extraction of physical insights from composite microstructures.
• Establish microstructure-property relationships at particle and network levels.

Main Methods:

• Machine learning (ML)-enabled framework.
• Automated transformation of multimodal X-ray images into topology-aware graphs.
• Graph analysis for extracting physical insights and microstructure-property relationships.

Main Results:

• Demonstrated ML framework for analyzing multiphase particulate composites.
• Corroborated the role of triple-phase junctions in electrochemical activity.
• Identified concurrent ion/electron conduction channels critical for performance.

Conclusions:

• Graph-based microstructure representation is a powerful paradigm.
• Bridges multimodal imaging with functional understanding.
• Facilitates data-driven materials design for particulate composites.