Accelerating design of synthesizable thermally conductive polymers using atomic interaction-embedded deep learning

International Journal of Heat and Mass Transfer 2026

View at publisher
Authors: Xiang Huang, Xizhao Xie, Congcong Luo, B. Yao, Yongqiang Chen, Weigang Ma
DOI: 10.1016/j.ijheatmasstransfer.2026.128585
Cited by: 2
References: 51

Fields of Study & Subjects

Machine Learning in Materials Science Thermal properties of materials Conducting polymers and applications

Publication details

Journal / source
International Journal of Heat and Mass Transfer
Publisher
Elsevier BV
Volume
262
Pages
128585–128585
Publication date
2 March 2026
Language
EN