文献类型：期刊文献

英文题名：Machine-Learning-Assisted Design of Highly Tough Thermosetting Polymers

作者：Hu, Yaxi[1];Zhao, Wenlin[1];Wang, Liquan[1];Lin, Jiaping[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 Ultra fin, Shanghai 200237, Peoples R China

年份：2022

卷号：14

期号：49

起止页码：55004

外文期刊名：ACS APPLIED MATERIALS & INTERFACES

收录：;EI(收录号：20225013228943);WOS:【SCI-EXPANDED(收录号:WOS:000897685700001)】；

基金：? ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (22173030, 51833003, 21975073, and 51621002) , Shanghai Scientific and Technological Innovation Projects (22ZR1417500 and 21511103102) , and Shanghai Aerospace Scientific and Technological Innovation Projects (SAST2019-120) .

语种：英文

外文关键词：thermosetting polymers; modulus; tensile strength; toughness; machine learning; materials genome approach

摘要：Despite advances in machine learning for accurately predicting material properties, forecasting the performance of thermosetting polymers remains a challenge due to the sparsity of historical experimental data and their complicated crosslinked structures. We proposed a machine-learning-assisted materials genome approach (MGA) for rapidly designing novel epoxy thermosets with excellent mechanical properties (high tensile moduli, high tensile strength, and high toughness) through high-throughput screening in a vast chemical space. Machine-learning models were established by combining attention- and gate-augmented graph convolutional networks, multilayer perceptrons, classical gel theory, and transfer learning from small molecules to polymers. Proof-of-concept experiments were carried out, and the structures designed by the MGA were verified. Gene substructures affecting the modulus, strength, and toughness were also extracted, revealing the mechanisms of polymers with high mechanical properties. The developed strategy can be employed to design other thermosetting polymers efficiently.