Machine learning-assisted systematical polymerization planning:case studies on reversible-deactivation radical polymerization

• 作者: Yu Gu Peirong Lin Chengda Zhou Mao Chen
• 来源期刊: 中国科学：化学（英文版）
• 年，卷（期）: 2021,（6）
• 所属期刊栏目: ARTICLES
• 页码范围: 1039-1046
• 页数: 8页
• 语种: 英文

摘要

The combined influence of chemical composition,molecular weight (MW) and molecular weight distribution (D) on the functions and performances of polymeric materials necessitates simultaneous satisfaction of multidimensional requirements during polymer synthesis.However,the complexity of polymerization reactions often dissuades chemists when precisely accessing diversified polymer targets.Herein,we developed a machine learning (ML)-assisted systematical polymerization planning (SPP) platform for addressing this challenge.With ML model providing integrated navigation of the reaction space,this approach can conduct multivariate analysis touncover complex interactions between the polymerization result and conditions,prescribing optimal reaction conditions to achieve discretionary polymer targets concerning three dimensions including chemical composition,MWand D values.Given the increasing importance of polymerization in advanced material engineering,this ML-assisted SPP platform provides a universal strategy to access tailored polymers with on-demand prediction of polymerization parameters.