4.6 Review

Application of Artificial Neural Networks for Catalysis: A Review

Journal

CATALYSTS
Volume 7, Issue 10, Pages -

Publisher

MDPI
DOI: 10.3390/catal7100306

Keywords

machine learning; artificial neural network (ANN); catalyst; catalysis; experiment; theory

Funding

  1. Open Fund of Key Laboratory of Low-grade Energy Utilization Technologies and Systems, Ministry of Education of China [LLEUTS-201708]
  2. Scientific and Technological Research Program of Chongqing Municipal Education Commission [KJ1709193]
  3. Open Fund of Key Laboratory of Low-grade Energy Utilization Technologies and Systems, Ministry of Education of China [LLEUTS-201708]
  4. Scientific and Technological Research Program of Chongqing Municipal Education Commission [KJ1709193]

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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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