Machine Learning for Chemistry:Basics and Applications

查看全文 作　　者：Yun-Fei [1]Shi;Zheng-Xin [1]Yang;Sicong [2]Ma;Pei-Lin [1]Kang;Cheng [1]Shang;[3]P.Hu;Zhi-Pan [1,2]Liu

机构地区：[1]Collaborative Innovation Center of Chemistry for Energy Material,Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials,Key Laboratory of Computational Physical Sciences of the Ministry of Education,Department of Chemistry,Fudan University,Shanghai 200433,China;[2]Key Laboratory of Synthetic and Self-Assembly Chemistry for Organic Functional Molecules,Shanghai Institute of Organic Chemistry,Chinese Academy of Sciences,Shanghai 200032,China;[3]School of Chemistry and Chemical Engineering,Queen’s University Belfast,Belfast BT95AG,UK

出　　处：《Engineering》索引2023年第8期,共14页

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)。 摘　　要：The past decade has seen a sharp increase in machine learning(ML)applications in scientific research.This 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 techniques.The described databases include some of the most popular chemical databases for molecules and materials obtained from either experiments or computational calculations.Important two-dimensional(2D)and three-dimensional(3D)features representing the chemical environment of molecules and solids are briefly introduced.Decision tree and deep learning neural network algorithms are overviewed to emphasize their frameworks and typical application scenarios.Three 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 exploration.Finally,a prospect on future ML applications is provided. 关键词：Machine learning Atomic simulation CATALYSIS Retrosynthesis Neural network potential

相关文献

参考文献（154）
引证文献（3）
耦合文献（159）

参考文献(154)

引证文献(3)

耦合文献(159)

互联网出版许可证新出网证(渝)字10号全国400电话 - 免长途话费

Machine Learning for Chemistry:Basics and Applications

参考文献(154)

引证文献(3)

耦合文献(159)

用户提示