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Open AccessFeature PaperReview

Application of Artificial Neural Networks for Catalysis: A Review

1,*,†,‡, 2,*,‡ and 3
College of Chemistry, Sichuan University, Chengdu 610064, China
School of Chemistry and Chemical Engineering, Chongqing University of Technology, Chongqing 400054, China
Department of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding 071003, China
Authors to whom correspondence should be addressed.
Present Address: Department of Chemistry and Institute for Computational and Engineering Sciences, The University of Texas at Austin, 105 E. 24th Street, Stop A5300, Austin, TX 78712, USA.
These authors contributed equally to this work.
Catalysts 2017, 7(10), 306;
Received: 28 September 2017 / Revised: 14 October 2017 / Accepted: 16 October 2017 / Published: 18 October 2017
PDF [7318 KB, uploaded 19 October 2017]


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. View Full-Text
Keywords: machine learning; artificial neural network (ANN); catalyst; catalysis; experiment; theory machine learning; artificial neural network (ANN); catalyst; catalysis; experiment; theory

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Li, H.; Zhang, Z.; Liu, Z. Application of Artificial Neural Networks for Catalysis: A Review. Catalysts 2017, 7, 306.

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