Multi-objective and cross-scale inverse design of temperature-control materials via physics-constrained machine learning
Year: 2026
Type: article
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 a DAG, a 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. Benefitting from the bifunctional role of the am-Al2O3 interlayer as both an interfacial phonon bridge and an 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.
Source: National Science Review
DOI: 10.1093/nsr/nwag503
Institutions: Chinese University of Hong Kong, Peking University, Xiamen University, Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, Shenzhen Institute of Advanced Electronic Materials, Northwestern Polytechnical University, Hunan University, Shaanxi University of Technology, Hong Kong University of Science and Technology, Shanghai Jiao Tong University, State Key Laboratory of Metal Matrix Composites
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,000
Funders: National Supercomputer Centre in Guangzhou, National Natural Science Foundation of China, Chinese Academy of Sciences, Innovation and Technology Commission, Chinese University of Hong Kong, Innovation and Technology Fund, National Key Research and Development Program of China, Science and Technology Planning Project of Guangdong Province
Awards: G3106941149, G3337197980, G4319310967, G6008064796, The Fundamental Research on Thermally Conductive Gel Interface Material and Its Application in Chiplet Heat Dissipation, The Fundamental Research on Thermally Conductive Gel Interface Material and Its Application in Chiplet Heat Dissipation, G8221598557, G89388175, G9000845281, G9000845562
OpenAlex ID: W7203965537