Multi-objective and cross-scale inverse design of temperature-control materials via physics-constrained machine learning

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ID: 325994
2026
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Abstract
Abstract Temperature-control materials, notably composite phase-change materials (CPCMs), show great potential for the thermal management of next-generation power electronics. However, the synergistic optimization of multiple performance metrics still relies heavily on Edisonian trial-and-error experimentation. Herein, we present an artificial intelligence framework that incorporates a physics-constrained inverse-design system (PHICS) built upon a directed acyclic graph (DAG) architecture for CPCMs, integrating interface, phase, and carrier engineering. By encoding structural hierarchies and physical causality through DAG, PHICS framework couples forward predictive modeling with a diversity-enhanced NSGA-II optimizer to efficiently map Pareto-optimal design boundaries. Guided by these predictions, we successfully fabricate a high-performance CPCM composed of an oriented graphite fiber skeleton and an n-octacosane matrix with amorphous alumina (am-Al2O3) interfacial transition layers. Benefiting from the bifunctional role of the am-Al2O3 interlayer as both an interfacial phonon bridge and electron barrier, the resulting CPCMs achieve a superior balance of thermal conduction, thermal storage, and electrical insulation. These results demonstrate the accuracy of PHICS-guided multifunctional composite design, establishing a closed-loop platform that combines physics-constrained machine learning with experimental validation to solve key thermal–electrical trade-offs.
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Authors Dongliang Ding, Minhao Zou, Ruoyu Huang, Ting Liang, Hang Shi, Ke Xu, Chunyu Wong, Yanhui Chen, Meng Han, Linfeng Yu, Guoxian Zhang, Yangbo Chen, Yimin Yao, X. T. Zhang, Yanguang Zhou, Chengyi Song, Xiaoliang Zeng, Yijie Peng, Tao Deng, Jianbin Xu, Rong Sun
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Journal national science review
Year 2026
DOI
10.1093/nsr/nwag503
URL
https://doi.org/10.1093/nsr/nwag503
Keywords Keywords not found

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