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Bridging the complexity gap in computational heterogeneous catalysis with machine learning

Nature Catalysis volume 6, pages 122–136 (2023)Cite this article

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Abstract

Heterogeneous catalysis underpins a wide variety of industrial processes including energy conversion, chemical manufacturing and environmental remediation. Significant advances in computational modelling towards understanding the nature of active sites and elementary reaction steps have occurred over the past few decades. The complexity gap between theory and experiment, however, remains overwhelming largely due to the limiting length and timescales of ab initio simulations, which severely impede the discovery of high-performance catalytic materials. This Review summarizes recent developments and applications of machine learning to narrow and, optimistically, bridge the gap created by the dynamic, mechanistic and chemostructural complexities inherent to the reactive interfaces of practical relevance. We foresee the prospects and challenges of machine learning for the automated design of sustainable catalytic technologies within a data-centric ecosystem that coevolves with computational and data sciences.

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Fig. 1: Bridging the theory–experiment gap in computational heterogeneous catalysis with machine learning.
Fig. 2: Data-enhanced atomistic thermodynamics for exploring configurational spaces.
Fig. 3: MLMD at extended length and timescales.
Fig. 4: Graph theoretical enumeration of reaction pathways with machine learning.
Fig. 5: Environment-aware microkinetics enabled by machine learning.
Fig. 6: Active machine learning for accelerating catalytic materials discovery.
Fig. 7: Design rules from interpretable machine learning.
Fig. 8: BO strategies to find optimal catalysts.

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Acknowledgements

H.S.P., S.W. and H.X. acknowledge funding support from the NSF CAREER programme (CBET-1845531). T.M. and X.H. acknowledge funding support from the NSF Chemical Catalysis programme (CHE-2102363). H.X. acknowledges the partial support from the US Department of Energy, Office of Basic Energy Sciences under contract no. DE-SC0023323. T.M. especially thanks the NSF Non-Academic Research Internships for Graduate Students (INTERN) programme for supporting his work in the Brookhaven National Lab chemistry division under the guidance of P. Liu. M.W. acknowledges funding support from the NSF EAGER programme (CBET-2103478). F.C. acknowledges the partial sponsorship by Department of Navy award N00014-22-1-2001 issued by the Office of Naval Research. The United States Government has a royalty-free license throughout the world in all copyrightable material contained herein.

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  1. These authors contributed equally: Tianyou Mou, Hemanth Somarajan Pillai, Siwen Wang.

Authors and Affiliations

  1. Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA

    Tianyou Mou, Hemanth Somarajan Pillai, Siwen Wang, Xue Han & Hongliang Xin

  2. Department of Chemical Engineering, University of Massachusetts Lowell, Lowell, MA, USA

    Mingyu Wan & Fanglin Che

  3. Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA

    Neil M. Schweitzer

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H.X., F.C. and N.S. conceived the idea of the Review. T.M., H.S.P. and S.W. led the manuscript writing. All the authors contributed to the revision of this manuscript.

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Mou, T., Pillai, H.S., Wang, S. et al. Bridging the complexity gap in computational heterogeneous catalysis with machine learning. Nat Catal 6, 122–136 (2023). https://doi.org/10.1038/s41929-023-00911-w

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