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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.16085 |
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| _version_ | 1866910224191848448 |
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| author | Li, Zebin Deng, Shimao Liu, Yijin Hu, Jia-Mian |
| author_facet | Li, Zebin Deng, Shimao Liu, Yijin Hu, Jia-Mian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_16085 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning Enabled Graph Analysis of Particulate Composites: Application to Solid-state Battery Cathodes Li, Zebin Deng, Shimao Liu, Yijin Hu, Jia-Mian Materials Science Computer Vision and Pattern Recognition 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. |
| title | Machine Learning Enabled Graph Analysis of Particulate Composites: Application to Solid-state Battery Cathodes |
| topic | Materials Science Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.16085 |