Application of Artificial Neural Networks for Catalysis: A Review

被引:163
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
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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共 106 条
  • [41] Neural network Analysis of Selective CO Oxidation over Copper-Based Catalysts for Knowledge Extraction from Published Data in the Literature
    Gunay, M. Erdem
    Yildirim, Ramazan
    [J]. INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2011, 50 (22) : 12488 - 12500
  • [42] Finding Nature's Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory
    Hautier, Geoffroy
    Fischer, Christopher C.
    Jain, Anubhav
    Mueller, Tim
    Ceder, Gerbrand
    [J]. CHEMISTRY OF MATERIALS, 2010, 22 (12) : 3762 - 3767
  • [43] ARTIFICIAL NEURAL NETWORKS
    HOPFIELD, JJ
    [J]. IEEE CIRCUITS AND DEVICES MAGAZINE, 1988, 4 (05): : 3 - 10
  • [44] Artificial neural network aided design of catalyst for propane ammoxidation
    Hou, ZY
    Dai, QL
    Wu, XQ
    Chen, GT
    [J]. APPLIED CATALYSIS A-GENERAL, 1997, 161 (1-2) : 183 - 190
  • [45] Trends in extreme learning machines: A review
    Huang, Gao
    Huang, Guang-Bin
    Song, Shiji
    You, Keyou
    [J]. NEURAL NETWORKS, 2015, 61 : 32 - 48
  • [46] Extreme learning machine: Theory and applications
    Huang, Guang-Bin
    Zhu, Qin-Yu
    Siew, Chee-Kheong
    [J]. NEUROCOMPUTING, 2006, 70 (1-3) : 489 - 501
  • [47] FireWorks: a dynamic workflow system designed for high-throughput applications
    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.
    [J]. 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
    Jung, Hyun Chul
    Kim, Jin Sung
    Heo, Hoon
    [J]. ENERGY AND BUILDINGS, 2015, 90 : 76 - 84
  • [49] Artificial neural networks used for the performance prediction of a thermosiphon solar water heater
    Kalogirou, SA
    Panteliou, S
    Dentsoras, A
    [J]. RENEWABLE ENERGY, 1999, 18 (01) : 87 - 99
  • [50] Applications of artificial neural networks in energy systems - A review
    Kalogirou, SA
    [J]. ENERGY CONVERSION AND MANAGEMENT, 1999, 40 (10) : 1073 - 1087