Application of Artificial Neural Networks for Catalysis: A Review

被引：169
作者：
Li, Hao [1 ,4 ,5 ]
Zhang, Zhien [2 ]
Liu, Zhijian [3 ]
机构：
[1] Sichuan Univ, Coll Chem, Chengdu 610064, Sichuan, Peoples R China
[2] Chongqing Univ Technol, Sch Chem & Chem Engn, Chongqing 400054, Peoples R China
[3] North China Elect Power Univ, Sch Energy Power & Mech Engn, Dept Power Engn, Baoding 071003, Peoples R China
[4] Univ Texas Austin, Dept Chem, 105 E 24th St,Stop A5300, Austin, TX 78712 USA
[5] Univ Texas Austin, Inst Computat & Engn Sci, 105 E 24th St,Stop A5300, Austin, TX 78712 USA
来源：
CATALYSTS | 2017年 / 7卷 / 10期
关键词：
machine learning; artificial neural network (ANN); catalyst; catalysis; experiment; theory; RESPONSE-SURFACE METHODOLOGY; DENSITY-FUNCTIONAL THEORY; SUPPORT VECTOR MACHINE; POTENTIAL-ENERGY SURFACES; PHOTO-FENTON PROCESS; GENETIC ALGORITHM; MOLECULAR-DYNAMICS; OXYGEN REDUCTION; SUNFLOWER OIL; CO OXIDATION;
D O I：
10.3390/catal7100306
中图分类号：
O64 [物理化学（理论化学）、化学物理学];
学科分类号：
070304 ; 081704 ;
摘要：
Machine learning has proven to be a powerful technique during the past decades. Artificial neural network (ANN), as one of the most popular machine learning algorithms, has been widely applied to various areas. However, their applications for catalysis were not well-studied until recent decades. In this review, we aim to summarize the applications of ANNs for catalysis research reported in the literature. We show how this powerful technique helps people address the highly complicated problems and accelerate the progress of the catalysis community. From the perspectives of both experiment and theory, this review shows how ANNs can be effectively applied for catalysis prediction, the design of new catalysts, and the understanding of catalytic structures.
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页数：19
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