Researchers Zebin Li, Shimao Deng, Yijin Liu, and Jia-Mian Hu from the University of California, San Diego have developed a novel approach to analyze and optimize the microstructure of particulate composites, with significant implications for the energy sector, particularly in solid-state battery development.
The team’s work focuses on the microstructural features of particulate composites, which are crucial in various chemical and electrochemical systems. These features, such as multiphase boundaries and inter-particle connections, greatly influence system performance. Advances in X-ray microscopy now allow for high-throughput capture of large-scale, multimodal images of these complex microstructures. However, utilizing these extensive datasets to gain new physical insights and guide microstructure optimization has been a persistent challenge.
To address this, the researchers developed a machine learning (ML) enabled framework that automatically transforms experimental multimodal X-ray images of multiphase particulate composites into scalable, topology-aware graphs. These graphs facilitate the extraction of physical insights and the establishment of local microstructure-property relationships at both the particle and network levels.
Using the multiphase particulate cathode of solid-state lithium batteries as an example, the team’s ML-enabled graph analysis confirmed the critical role of triple phase junctions and concurrent ion/electron conduction channels in achieving desirable local electrochemical activity. This work establishes graph-based microstructure representation as a powerful paradigm for bridging multimodal experimental imaging and functional understanding. It also paves the way for microstructure-aware, data-driven materials design in a broad range of particulate composites.
The practical applications for the energy sector are significant. By better understanding and optimizing the microstructure of battery components, researchers can enhance the performance and efficiency of solid-state batteries. This could lead to improvements in energy storage, which is a critical aspect of renewable energy integration and electric vehicle development. The research was published in the journal Nature Communications.
This article is based on research available at arXiv.