文献类型：期刊文献

英文题名：Machine learning for mechanics prediction of 2D MXene-based aerogels

作者：Rong, Chao[1,2,3];Zhou, Lei[1,2,3];Zhang, Bowei[1,2,3];Xuan, Fu-Zhen[1,2,3]

机构：[1]East China Univ Sci & Technol, Shanghai Key Lab Intelligent Sensing & Detect Tech, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Minist Educ, Key Lab Pressure Syst & Safety, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai 200237, Peoples R China

年份：2023

卷号：38

外文期刊名：COMPOSITES COMMUNICATIONS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000914262900001)】；

基金：Acknowledgments This work was supported by the National Natural Science Foundation of China (Grant. No. 52105145, No. 12274124) and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Hybrid aerogel; Mechanical properties; Machine learning prediction; Importance analysis; Prediction accuracy

摘要：Hybrid aerogels of two-dimensional (2D) transition metal carbide (MXene) and nanocellulose show huge po-tential in a wide range of applications owing to their unique compressive mechanical properties. However, the compressive mechanical properties of hybrid aerogels are sensitive to the physical parameters of its building blocks, which are difficult to be optimized by high throughput experiments. Considering the inherent complex variables of MXene/nanocellulose aerogels, this work realizes the prediction of their mechanical properties by machine learning (ML). Based on the reported 34 sets of data on Ti3C2 MXene, we trained three ML algorithms: artificial neural network (ANN), support vector machine (SVM) and random forest (RF). Results indicate that the ANN outperforms other algorithms as it fits various nonlinear input features well. The relative content of Ti3C2 is the most effective factor in the compressive strength of hybrid aerogel. The mechanical properties of the 540 input possibilities are predicted by the outperforming ANN model, and quantitative structural adjustment is obtained for a maximum compression modulus of 29 kPa. This work provides guideline for the mechanical property prediction of composite materials using ML.