Machine Learning for Chemistry:Basics and Applications
作者机构：Collaborative Innovation Center of Chemistry for Energy MaterialShanghai Key Laboratory of Molecular Catalysis and Innovative MaterialsKey Laboratory of Computational Physical Sciences of the Ministry of EducationDepartment of ChemistryFudan UniversityShanghai 200433China Key Laboratory of Synthetic and Self-Assembly Chemistry for Organic Functional MoleculesShanghai Institute of Organic ChemistryChinese Academy of SciencesShanghai 200032China School of Chemistry and Chemical EngineeringQueen’s University BelfastBelfast BT95AGUK
出 版 物：《Engineering》 (工程（英文）)
年 卷 期：2023年第27卷第8期
页 面：70-83页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 0703[理学-化学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：financial support from the National Key Research and Development Program of China(2018YFA0208600) the National Natural Science Foundation of China(12188101,22033003,91945301,91745201,92145302,22122301,and 92061112) the Tencent Foundation for XPLORER PRIZE,and Fundamental Research Funds for the Central Universities(20720220011)
主 题：Machine learning Atomic simulation Catalysis Retrosynthesis Neural network potential
摘 要：The past decade has seen a sharp increase in machine learning(ML)applications in scientific *** review introduces the basic constituents of ML,including databases,features,and algorithms,and highlights a few important achievements in chemistry that have been aided by ML *** described databases include some of the most popular chemical databases for molecules and materials obtained from either experiments or computational *** two-dimensional(2D)and three-dimensional(3D)features representing the chemical environment of molecules and solids are briefly *** tree and deep learning neural network algorithms are overviewed to emphasize their frameworks and typical application *** important fields of ML in chemistry are discussed:(1)retrosynthesis,in which ML predicts the likely routes of organic synthesis;(2)atomic simulations,which utilize the ML potential to accelerate potential energy surface sampling;and(3)heterogeneous catalysis,in which ML assists in various aspects of catalytic design,ranging from synthetic condition optimization to reaction mechanism ***,a prospect on future ML applications is provided.
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