文献类型：期刊文献

英文题名：Machine-learning-assisted multiscale modeling strategy for predicting mechanical properties of carbon fiber reinforced polymers

作者：Zhao, Guomei[1];Xu, Tianhao[1];Fu, Xuemeng[1];Zhao, Wenlin[1];Wang, Liquan[1];Lin, Jiaping[1];Hu, Yaxi[1];Du, Lei[1]

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

年份：2024

卷号：248

外文期刊名：COMPOSITES SCIENCE AND TECHNOLOGY

收录：;EI(收录号：20240515476473);WOS:【SCI-EXPANDED(收录号:WOS:001177787500001)】；

基金：This work was supported by the National Natural Science Foundation of China (22173030, 52394271, 51833003, and 21975073) and Shanghai Scientific and Technological Innovation Projects (22ZR1417500 and 21511103102) .

语种：英文

外文关键词：Machine learning; Multiscale modeling; Mechanical properties; Resins; CFRPs

摘要：Carbon fiber reinforced polymers (CFRPs) possess light weight and high strength, making them highly attractive for various applications. However, the design parameter space of CFRPs is extensive, with the complex relationship between structures and mechanical properties. Traditional design methods that rely on trial and error or scientific intuition are laborious and expensive for achieving optimal properties of CFRPs. In light of this challenge, we proposed a machine-learning-assisted multiscale modeling strategy that can efficiently predict the mechanical properties of CFRPs. This strategy uses low-computational-cost machine learning (ML) models to replace traditional theoretical models and combines them with molecular dynamics simulation to predict the mechanical properties of CFRPs starting from resin molecules. Comparing predicted values with the proof-ofconcept experiment and the existing experimental findings showed that the predicted values of the ML model are in good agreement with the experimental ones. This strategy can be a viable machine-learning-assisted solution to designing CFRPs.