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

• URL: http://arxiv.org/abs/2512.16085v1
• Date: Thu, 18 Dec 2025 02:00:22 GMT
• Title: Machine Learning Enabled Graph Analysis of Particulate Composites: Application to Solid-state Battery Cathodes
• Authors: Zebin Li, Shimao Deng, Yijin Liu, Jia-Mian Hu,
• Abstract summary: 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.<n>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.
• Score: 9.342098489571326
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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