文献类型：期刊文献

英文题名：Design of silicon-containing arylacetylene resins aided by machine learning enhanced materials genome approach

作者：Zhang, Songqi[1];Du, Shi[1];Wang, Liquan[1];Lin, Jiaping[1];Du, Lei[1];Xu, Xinyao[1];Gao, Liang[1]

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

年份：2022

卷号：448

外文期刊名：CHEMICAL ENGINEERING JOURNAL

收录：;EI(收录号：20222612280504);WOS:【SCI-EXPANDED(收录号:WOS:000879314200003)】；

基金：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：Materials genome approach; Machine learning; Silicon-containing acetylene resin; Heat resistance

摘要：Silicon-containing acetylene resins have a broad application prospect as a type of organic-inorganic hybrid high-temperature resistant resins. However, its processability still needs further improvement to meet processing requirements for low viscosity. We proposed a materials genome approach to design and screen silicon-containing acetylene resins with excellent processing properties and heat resistance. To high-throughput screen the promising resin, we established machine learning models for predicting the properties of processing and heat resistance. Ten latent resins were screened, and one easy-to-synthesize resin was prepared by the Grignard reagent method to verify the materials genome approach. The results showed that the processing properties of the screened resin are improved evidently, with excellent heat resistance maintained. This work generates a fresh way for cost-effective data-driven designs of silicon-containing acetylene resins.