Evidence map›Paper›PMID 42819145›Full record

ArticleNational science review2026

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

Dongliang Ding, Minhao Zou, Ruoyu Huang, Ting Liang, Hang Shi, Ke Xu, Chunyu Wong, Yanhui Chen, Meng Han, Linfeng Yu and 11 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in National science review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Dongliang DingDepartment of Electronics Engineering, The Chinese University of Hong Kong, Hong Kong 999077, China.ORCID https://orcid.org/0009-0009-9546-4265
Minhao ZouSchool of Computer Science, Peking University, Beijing 100871, China.
Ruoyu HuangCollege of Physical Science and Technology, Xiamen University, Xiamen 361000, China.
Ting LiangDepartment of Electronics Engineering, The Chinese University of Hong Kong, Hong Kong 999077, China.
Hang ShiShenzhen Institute of Advanced Electronic Materials, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Ke XuDepartment of Electronics Engineering, The Chinese University of Hong Kong, Hong Kong 999077, China.
Chunyu WongDepartment of Electronics Engineering, The Chinese University of Hong Kong, Hong Kong 999077, China.
Yanhui ChenNingbo Institute of Northwestern Polytechnical University, Ningbo 315103, China.
Meng HanShenzhen Institute of Advanced Electronic Materials, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Linfeng YuCollege of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
Guoxian ZhangSchool of Materials Science and Engineering, Shaanxi University of Technology, Hanzhong 723001, China.
Yangbo ChenCollege of Physical Science and Technology, Xiamen University, Xiamen 361000, China.
Yimin YaoShenzhen Institute of Advanced Electronic Materials, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Xueao ZhangCollege of Physical Science and Technology, Xiamen University, Xiamen 361000, China.
Yanguang ZhouDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Hong Kong 999077, China.
Chengyi SongState Key Laboratory of Metal Matrix, Composites School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Xiaoliang ZengShenzhen Institute of Advanced Electronic Materials, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Yijie PengAdvanced Institute of Information Technology, Peking University, Hangzhou 311200, China.
Tao DengState Key Laboratory of Metal Matrix, Composites School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Jianbin XuDepartment of Electronics Engineering, The Chinese University of Hong Kong, Hong Kong 999077, China.ORCID https://orcid.org/0000-0003-0509-9508
Rong SunShenzhen Institute of Advanced Electronic Materials, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Indexed as

amorphous aluminacarrier engineeringcomposite phase-change materialsinterface engineeringphysics-constrained machine learning

Identifiers

PMID42819145
PMCPMC13625007

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.