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Machine learning for heterogeneous catalyst design and discovery
Author(s) -
Goldsmith Bryan R.,
Esterhuizen Jacques,
Liu JinXun,
Bartel Christopher J.,
Sutton Christopher
Publication year - 2018
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.16198
Subject(s) - dept , library science , chemistry , computer science , stereochemistry
Advances in machine learning (ML) are making a large impact in many fields, including: artificial intelligence, materials science, and chemical engineering. Generally, ML tools learn from data to find insights or make fast predictions of target properties. Recently, ML is also greatly influencing heterogeneous catalysis research due to the availability of ML (e.g., Python Scikit-learn, TensorFlow) and workflow management tools (e.g., ASE, Atomate), the growing amount of data in materials databases (e.g., Novel Materials Discovery Laboratory, Citrination, Materials Project, CatApp), and algorithmic improvements. New catalysts are needed for sustainable chemical production, alternative energy, and pollution mitigation applications to meet the demands of our world’s rising population. It is a challenging endeavor, however, to make novel heterogeneous catalysts with good performance (i.e., stable, active, selective) because their performance depends on many properties: composition, support, surface termination, particle size, particle morphology, and atomic coordination environment. Additionally, the properties of heterogeneous catalysts can change under reaction conditions through various phenomena such as Ostwald ripening, particle disintegration, surface oxidation, and surface reconstruction. Many heterogeneous catalyst structures are disordered or amorphous in their active state, which further complicates their atomic-level characterization by modeling and experiment. Computational modeling using quantum mechanical (QM) methods such as density functional theory (DFT) can accelerate catalyst screening by enabling rapid prototyping and revealing active sites and structure-activity relations. The high computational cost of QM methods, however, limits the range of catalyst spaces that can be examined. Recent progress in merging ML with QM modeling and experiments promises to drive forward rational catalyst design. Therefore, it is timely to highlight the ability of ML tools to accelerate