文献类型：期刊文献

英文题名：Discovery of thermosetting polymers with low hygroscopicity, low thermal expansivity, and high modulus by machine learning

作者：Xu, Xinyao[1];Zhao, Wenlin[1];Hu, Yaxi[1];Wang, Liquan[1];Lin, Jiaping[1];Qi, Huimin[1];Du, Lei[1]

机构：[1]East China Univ Sci & Technol, Frontiers Sci Ctr Materiobiol & Dynam Chem, Sch Mat Sci & Engn, Shanghai Key Lab Adv Polymer Mat,Key Lab Ultrane M, Shanghai 200237, Peoples R China

年份：2023

卷号：11

期号：24

起止页码：12918

外文期刊名：JOURNAL OF MATERIALS CHEMISTRY A

收录：;EI(收录号：20230913657706);WOS:【SCI-EXPANDED(收录号:WOS:000937436300001)】；

基金：X. X. and W. Z. contributed equally to this work. This work was supported by the National Natural Science Foundation of China (51833003, 22173030, 21975073, and 51621002).

语种：英文

外文关键词：Thermosets

摘要：Traditional material design principles for discovering thermosetting polymers, such as polycyanurates with a combination of low hygroscopicity, low thermal expansivity, and high modulus, are inefficient due to the intrinsic conflict among properties. Machine learning (ML) can be employed to overcome this conflict by exploring the vast polymer space in silico but it is limited by the lack of polymer data. This work demonstrates that through multi-fidelity learning, limited experimental data can be well utilized with the assistance of all-atomic simulation data for establishing robust ML-based property estimation models. We screen the virtual candidates obtained from combinations of "polymer genes" through well-trained ML-based models. Several promising polycyanurates are demonstrated to have remarkable properties, as verified by theoretical simulations and proof-of-concept experiments. Furthermore, structural analysis is performed to deduce the underlying physics. The proposed framework can be extended to accelerate the rational design of crosslinked polymers with desired properties and reveal fundamental physical rules governing the design.