Machine Learning for Computational Heterogeneous Catalysis

Schlexer Lamoureux, Philomena; Winther, Kirsten T.; Garrido Torres, Jose Antonio; Streibel, Verena; Zhao, Meng; Bajdich, Michal; Abild‐Pedersen, Frank; Bligaard, Thomas

doi:10.1002/cctc.201900595

Title: Machine Learning for Computational Heterogeneous Catalysis

Journal Article · 18 June 2019 · ChemCatChem

DOI: https://doi.org/10.1002/cctc.201900595 · OSTI ID:1561362

Schlexer Lamoureux, Philomena ^[1]; Winther, Kirsten T. ^[1]; Garrido Torres, Jose Antonio ^[1]; Streibel, Verena ^[1]; Zhao, Meng ^[1]; Bajdich, Michal ^[1]; Abild‐Pedersen, Frank ^[1]; Bligaard, Thomas ^[1]

SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory 2575 Sand Hill Road, Menlo Park California 94025 United States, Department of Chemical Engineering Stanford University 443 Via Ortega Stanford CA 94305 United States

Abstract Big data and artificial intelligence has revolutionized science in almost every field – from economics to physics. In the area of materials science and computational heterogeneous catalysis, this revolution has led to the development of scientific data repositories, as well as data mining and machine learning tools to investigate the vast materials space. The goal of using these tools is to establish a deeper understanding of the relations between materials properties and activity, selectivity and stability – the important figures of merit in catalysis. Based on these insights, catalyst design principles can be established, which hopefully lead us to discover highly efficient catalysts to solve pressing issues for a sustainable future and the synthesis of highly functional materials, chemicals and pharmaceuticals. The inherent complexity of catalytic reactions quests for machine learning methods to efficiently navigate through the high‐dimensional hyper‐surfaces in structure optimization problems to determine relevant chemical structures and transition states. In this review, we show how cutting edge data infrastructures and machine learning methods are being used to address problems in computational heterogeneous catalysis.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: NONE; AC02-76SF00515

OSTI ID:: 1561362

Journal Information:: ChemCatChem, Journal Name: ChemCatChem Journal Issue: 16 Vol. 11; ISSN 1867-3880

Publisher:: Wiley Blackwell (John Wiley & Sons)Copyright Statement

Country of Publication:: Germany

Language:: English

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