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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\u2013electrical trade-offs.<\/jats:p>","DOI":"10.1093\/nsr\/nwag503","type":"journal-article","created":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T11:44:42Z","timestamp":1787399082000},"source":"Crossref","is-referenced-by-count":0,"title":["Multi-objective and cross-scale inverse design of temperature-control materials via physics-constrained machine learning"],"prefix":"10.1093","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-9546-4265","authenticated-orcid":false,"given":"Dongliang","family":"Ding","sequence":"first","affiliation":[{"name":"The Chinese University of Hong Kong Department of Electronics Engineering, , Hong Kong 999077 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