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Progress in Computational and Machine‐Learning Methods for Heterogeneous Small‐Molecule Activation
Author(s) -
Gu Geun Ho,
Choi Changhyeok,
Lee Yeunhee,
Situmorang Andres B.,
Noh Juhwan,
Kim YongHyun,
Jung Yousung
Publication year - 2020
Publication title -
advanced materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 10.707
H-Index - 527
eISSN - 1521-4095
pISSN - 0935-9648
DOI - 10.1002/adma.201907865
Subject(s) - catalysis , biochemical engineering , density functional theory , molecule , nanotechnology , rational design , heterogeneous catalysis , computer science , small molecule , materials science , computational model , field (mathematics) , stability (learning theory) , computational chemistry , chemistry , artificial intelligence , machine learning , organic chemistry , engineering , mathematics , biochemistry , pure mathematics
The chemical conversion of small molecules such as H 2 , H 2 O, O 2 , N 2 , CO 2 , and CH 4 to energy and chemicals is critical for a sustainable energy future. However, the high chemical stability of these molecules poses grand challenges to the practical implementation of these processes. In this regard, computational approaches such as density functional theory, microkinetic modeling, data science, and machine learning have guided the rational design of catalysts by elucidating mechanistic insights, identifying active sites, and predicting catalytic activity. Here, the theory and methodologies for heterogeneous catalysis and their applications for small‐molecule activation are reviewed. An overview of fundamental theory and key computational methods for designing catalysts, including the emerging data science techniques in particular, is given. Applications of these methods for finding efficient heterogeneous catalysts for the activation of the aforementioned small molecules are then surveyed. Finally, promising directions of the computational catalysis field for further outlooks are discussed, focusing on the challenges and opportunities for new methods.