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Roadmap for transforming heterogeneous catalysis with artificial intelligence

Nature Catalysis (2026)Cite this article

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Abstract

Artificial intelligence (AI) is poised to transform heterogeneous catalysis, opening avenues for catalytic materials discovery. By uncovering intricate patterns in high-dimensional data, AI has been reshaping our pursuit of sustainable catalytic processes across the energy, environmental and chemical sectors. This promise, however, hinges on overcoming fundamental barriers, including limitations in data availability and quality, challenges in the generalizability and interpretability of data-augmented decisions, and the persistent gap between in silico predictions and experiments. Here we outline a forward-looking roadmap for deeply integrating AI into heterogeneous catalysis with an AI-ready data ecosystem, multimodal foundation models, and ultimately autonomous laboratories to accelerate the development of next-generation catalytic technologies via AI-empowered human–machine collaboration.

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Fig. 1: Roadmap for integrating AI into heterogeneous catalysis.
Fig. 2: Building an AI-ready data ecosystem for heterogeneous catalysis.
Fig. 3: ML strategies underpinning multimodal foundation models for heterogeneous catalysis.
Fig. 4: Core pillars of autonomous laboratories in heterogeneous catalysis.

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Acknowledgements

We acknowledge funding support from the National Science Foundation under award no. 2409631, provided jointly by the Catalysis Program (R. McCabe) in the Division of Chemical, Bioengineering, Environmental, and Transport Systems (CBET) and the Chemical Catalysis Program (K. Moloy) in the Division of Chemistry (CHE), for the AI for Multidisciplinary Exploration and Discovery (AIMED) in Heterogeneous Catalysis Workshop. H.X. acknowledges funding support from the US Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Chemical Sciences, Geosciences and Biosciences Division (DE-SC0023323) and from the National Science Foundation under award no. 2245402 through the CDS&E Program and the CBET Catalysis Program. N.M.S. acknowledges the Big Ten Conference Center for providing meeting space and technical support for the workshop. W.J.S. acknowledges funding from the US DOE, Office of Science, Office of BES, Division of Chemical Sciences, Geosciences and Biosciences (FWP 47319). N.L. acknowledges PID2024-157556OB-I00, funded by MICIU/AEI/10.13039/501100011033/FEDER, UE, BSC-RES and EuroHPC-JU. T.L. acknowledges funding from NCCR Catalysis (grant nos. 180544 and 225147), a National Centre of Competence in Research funded by the Swiss National Science Foundation. L.C.G. acknowledges support from the National Science Foundation under award no. 2401067. L.Q. is supported by the US Department of Energy (DOE), Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences, and Biosciences, Catalysis Science program. Ames National Laboratory is operated for the US DOE by Iowa State University under contract no. DE-AC02-07CH11358. G.T.K.K.G. acknowledges start-up funding support from the School of Sustainable Chemical, Biological and Materials Engineering at the University of Oklahoma. We are also grateful to D. J. Kleinbaum and J. Hostetler from Emerald Cloud Lab, Tian Xie (Microsoft Research) and P. Majumdar (Core R&D, Dow) for insightful discussions.

Author information

Authors and Affiliations

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

    Hongliang Xin

  2. Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    John R. Kitchin

  3. Institute of Chemical Research of Catalonia (ICIQ-CERCA), The Barcelona Institute of Science and Technology, Tarragona, Spain

    Núria López

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

    Neil M. Schweitzer

  5. Debye Institute for Nanomaterials Science, Utrecht University, Utrecht, Netherlands

    Nongnuch Artrith

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

    Fanglin Che

  7. William A. Brookshire Department of Chemical and Biomolecular Engineering and Texas Center for Superconductivity (TcSUH), University of Houston, Houston, TX, USA

    Lars C. Grabow

  8. School of Sustainable Chemical, Biological and Materials Engineering, University of Oklahoma, Norman, OK, USA

    G. T. Kasun Kalhara Gunasooriya

  9. Department of Chemical Engineering and Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA

    Heather J. Kulik

  10. IBM Research Europe – Zurich, Rüschlikon, Switzerland

    Teodoro Laino

  11. NCCR Catalysis, Zurich, Switzerland

    Teodoro Laino

  12. Advanced Institute for Materials Research (WPI-AIMR), Tohoku University, Sendai, Miyagi, Japan

    Hao Li

  13. Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA

    Suljo Linic

  14. School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Andrew J. Medford

  15. ExxonMobil Technology and Engineering, Annandale, NJ, USA

    Randall J. Meyer

  16. Department of Materials Design and Innovation, University at Buffalo, Buffalo, NY, USA

    Jiayu Peng

  17. Advanced Research Project Agency – Energy, US Department of Energy, Washington, DC, USA

    Cory Phillips

  18. Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Jin Qian

  19. US DOE Ames National Laboratory, Iowa State University, Ames, IA, USA

    Long Qi

  20. Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA

    Wendy J. Shaw

  21. Fundamental AI Research (FAIR), Meta, San Francisco, CA, USA

    Zachary W. Ulissi

  22. Toyota Research Institute of North America, Ann Arbor, MI, USA

    Siwen Wang

  23. Beijing Key Laboratory of Artificial Intelligence for Advanced Chemical Engineering Materials, Department of Chemical Engineering, Tsinghua University, Beijing, China

    Xiaonan Wang

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  1. Hongliang Xin
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Contributions

H.X., J.R.K., N.L., and N.M.S. conceived the article and jointly led the development of the roadmap. H.X. coordinated the writing process. All authors (H.X., J.R.K., N.L., N.M.S., N.A., F.C., L.C.G., G.T.K.K.G., H.J.K., T.L., H.L., S.L., A.J.M., R.J.M., J.P., C.P., J.Q., L.Q., W.J.S., Z.W.U., S.W., and X.W.) contributed ideas, domain expertise, and specific content to the manuscript, and all authors reviewed, edited, and approved the final version.

Corresponding authors

Correspondence to Hongliang Xin, John R. Kitchin, Núria López or Neil M. Schweitzer.

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Xin, H., Kitchin, J.R., López, N. et al. Roadmap for transforming heterogeneous catalysis with artificial intelligence. Nat Catal (2026). https://doi.org/10.1038/s41929-026-01479-x

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