Machine learning for molecular thermodynamics

作     者：Jiaqi Ding Nan Xu Manh Tien Nguyen Qi Qiao Yao Shi Yi He Qing Shao Jiaqi Ding;Nan Xu;Manh Tien Nguyen;Qi Qiao;Yao Shi;Yi He;Qing Shao

作者机构：College of Chemical and Biological EngineeringZhejiang UniversityHangzhou 310027China Chemical and Materials Engineering DepartmentUniversity of KentuckyLexingtonKY 40506USA Key Laboratory of Biomass Chemical Engineering of Ministry of EducationZhejiang UniversityHangzhou 310027China Department of Chemical EngineeringUniversity of WashingtonSeattleWA 98195USA 

出 版 物：《Chinese Journal of Chemical Engineering》 (中国化学工程学报（英文版）)

年 卷 期：2021年第34卷第3期

页      面：227-239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)] 081704[工学-应用化学] 081104[工学-模式识别与智能系统] 08[工学] 0817[工学-化学工程与技术] 081701[工学-化学工程] 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 supports from the National Natural Science Foundation of China(21676245 and 51933009) the National Key Research and Development Program of China(2017YFB0702502) the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang(2019R01006) financial support provided by the Startup Funds of the University of Kentucky 

主　　题：Machine learning Thermodynamic properties Molecular engineering Molecular simulation Force field 

摘      要：Thermodynamic properties of complex systems play an essential role in developing chemical engineering *** remains a challenge to predict the thermodynamic properties of complex systems in a wide range and describe the behavior of ions and molecules in complex *** learning emerges as a powerful tool to resolve this issue because it can describe complex relationships beyond the capacity of traditional mathematical *** minireview will summarize some fundamental concepts of machine learning methods and their applications in three aspects of the molecular thermodynamics using several *** first aspect is to apply machine learning methods to predict the thermodynamic properties of a broad spectrum of systems based on known *** second aspect is to integer machine learning and molecular simulations to accelerate the discovery of *** third aspect is to develop machine learning force field that can eliminate the barrier between quantum mechanics and all-atom molecular dynamics *** applications in these three aspects illustrate the potential of machine learning in molecular thermodynamics of chemical *** will also discuss the perspective of the broad applications of machine learning in chemical engineering.