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.
引用
收藏
页数：19
相关论文
共 106 条
[41]   Neural network Analysis of Selective CO Oxidation over Copper-Based Catalysts for Knowledge Extraction from Published Data in the Literature [J].
Gunay, M. Erdem ;
Yildirim, Ramazan .
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2011, 50 (22) :12488-12500
[42]   Finding Nature's Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory [J].
Hautier, Geoffroy ;
Fischer, Christopher C. ;
Jain, Anubhav ;
Mueller, Tim ;
Ceder, Gerbrand .
CHEMISTRY OF MATERIALS, 2010, 22 (12) :3762-3767
[43]   ARTIFICIAL NEURAL NETWORKS [J].
HOPFIELD, JJ .
IEEE CIRCUITS AND DEVICES MAGAZINE, 1988, 4 (05) :3-10
[44]   Artificial neural network aided design of catalyst for propane ammoxidation [J].
Hou, ZY ;
Dai, QL ;
Wu, XQ ;
Chen, GT .
APPLIED CATALYSIS A-GENERAL, 1997, 161 (1-2) :183-190
[45]   Trends in extreme learning machines: A review [J].
Huang, Gao ;
Huang, Guang-Bin ;
Song, Shiji ;
You, Keyou .
NEURAL NETWORKS, 2015, 61 :32-48
[46]   Extreme learning machine: Theory and applications [J].
Huang, Guang-Bin ;
Zhu, Qin-Yu ;
Siew, Chee-Kheong .
NEUROCOMPUTING, 2006, 70 (1-3) :489-501
[47]   FireWorks: a dynamic workflow system designed for high-throughput applications [J].
Jain, Anubhav ;
Ong, Shyue Ping ;
Chen, Wei ;
Medasani, Bharat ;
Qu, Xiaohui ;
Kocher, Michael ;
Brafman, Miriam ;
Petretto, Guido ;
Rignanese, Gian-Marco ;
Hautier, Geoffroy ;
Gunter, Daniel ;
Persson, Kristin A. .
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2015, 27 (17) :5037-5059
[48]   Prediction of building energy consumption using an improved real coded genetic algorithm based least squares support vector machine approach [J].
Jung, Hyun Chul ;
Kim, Jin Sung ;
Heo, Hoon .
ENERGY AND BUILDINGS, 2015, 90 :76-84
[49]   Artificial neural networks used for the performance prediction of a thermosiphon solar water heater [J].
Kalogirou, SA ;
Panteliou, S ;
Dentsoras, A .
RENEWABLE ENERGY, 1999, 18 (01) :87-99
[50]   Applications of artificial neural networks in energy systems - A review [J].
Kalogirou, SA .
ENERGY CONVERSION AND MANAGEMENT, 1999, 40 (10) :1073-1087
12345678910