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2篇 您的检索式：作者名="Chengda Zhou"

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		题名	作者	年代	出处	被引量
	1	Machine learning-assisted systematical polymerization planning:case studies on reversible-deactivation radical polymerization显示文摘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 to uncover complex interactions between the polymerization result and conditions,prescribing optimal reaction conditions to achieve discretionary polymer targets concerning three dimensions including chemical composition,MWandD 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.	Yu Gu Peirong Lin Chengda Zhou Mao Chen	2021	Science China Chemistry2021，64，6：	2
	2	Shock-induced bct-bcc transition and melting of tin identified by sound velocity measurements显示文摘	Hu Jianbo Zhou Xianming Dai Chengda	2008	Journal of Applied Physics2008，104，08：	1

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