机器学习在聚烯烃催化剂领域的应用与展望  

Application and prospect of machine learning in polyolefin catalysts

作　　者：杨文泓[1,2] 孙文华[2,3] Wenhong Yang;Wen-Hua Sun(Advanced Materials Research Center,Petrochemical Research Institute,China National Petroleum Corporation,Beijing 102206,China;Key Laboratory of Engineering Plastics,Beijing National Laboratory for Molecular Science,Institute of Chemistry,Chinese Academy of Sciences,Beijing 100190,China;School of Chemical Sciences,University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国石油天然气股份有限公司石油化工研究院,新材料生物化工研究所,北京102206 [2]中国科学院化学研究所,北京分子科学国家实验室,工程塑料重点实验室,北京100190 [3]中国科学院大学化学科学学院,北京100049

出　　处：《科学通报》2022年第17期1870-1880,共11页Chinese Science Bulletin

基　　金：中国科学院化学研究所创新培育项目(CXPY19);国家自然科学基金(21871275)资助。

摘　　要：催化剂是决定聚烯烃的工业效率以及实现聚烯烃高端化的核心.传统开发催化剂的过程采用试错法,不仅实验步骤多、研发周期长,且催化性能的研究需要消耗大量资源.单纯依靠实验的分析方法很难挖掘出催化剂结构与聚合性能之间的内在关系.高水平的量子化学计算可以准确地获取反应机理,但针对宏量的实验数据,昂贵的计算成本是其局限.大数据时代,人工智能的发展势不可挡.机器学习作为人工智能的核心策略表现出强大的预测能力,并在科学、技术以及工业等各个领域获得了广泛的应用与发展.本文主要介绍机器学习在聚烯烃催化剂中的最新研究进展,并简要评述机器学习应用于烯烃催化中面临的机遇与挑战.Industrial and academic research has been extensively inspired by the ever-growing demand for polyolefin with high performance due to its special physical and mechanical properties,which has been widely applied in the area of engineering plastics,elastomer and high grade lubricants.Transition metal complex catalysts,which can make the olefin polymerization reaction feasible,have been one of the key techniques to produce polyolefin with various structures and properties.Although many fruitful reports are available describing different attempts on enhancing the performance of polyolefin catalyst by the means of the alteration of the ligand frameworks,shuffling the substituents as well as introducing new ligands.Nevertheless,the traditional process of catalyst development,using the trial-and-error method,usually needs long experimental steps and periods.Meanwhile,the measurement of catalytic performance is high cost and needs a lot of resources as well.Machine learning,as the core strategy of artificial intelligence,has shown strong predictive power in many fields of science and technology.However,the application in chemistry,especially in catalysis,is still in its infancy.Relying on the rapid development of different algorithms and computer hardware,it is the right time to harvest the potential of machine learning in the field of catalysis across academy and industry.Herein,we discuss the recent advances in the field of polyolefin catalysts by using machine learning methods,including Ziegler-Natta catalysts,phosphine monocyclic imine Cr(P,N)catalysts,ansa zirconocene catalysts,and late-transition metal complex catalysts.The catalytic performance is well predicted,providing insight into the underlying mechanism of the relationship between the micro-structure of the catalyst and its macro-performance at the molecule level.Tracing the recent progress,the report of machine learning in polyolefin catalysts is relatively few.One of the main reasons is that the experimental study of catalytic performance is time-consuming

关 键 词：聚烯烃催化剂 催化性能 分子描述符 机器学习 定量构效 

分 类 号：TQ325.1[化学工程—合成树脂塑料工业] TQ426[自动化与计算机技术—控制理论与控制工程] TP181[自动化与计算机技术—控制科学与工程]