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Roadmap for transforming heterogeneous catalysis with artificial intelligence

  • Hongliang Xin
  • , John R. Kitchin
  • , Núria López
  • , Neil M. Schweitzer
  • , Nongnuch Artrith
  • , Fanglin Che
  • , Lars C. Grabow
  • , G. T.Kasun Kalhara Gunasooriya
  • , Heather J. Kulik
  • , Teodoro Laino
  • , Hao Li
  • , Suljo Linic
  • , Andrew J. Medford
  • , Randall J. Meyer
  • , Jiayu Peng
  • , Cory Phillips
  • , Jin Qian
  • , Long Qi
  • , Wendy J. Shaw
  • , Zachary W. Ulissi
  • Siwen Wang, Xiaonan Wang
  • Virginia Polytechnic Institute and State University
  • Carnegie Mellon University
  • The Barcelona Institute of Science and Technology
  • Northwestern University
  • Utrecht University
  • University of Massachusetts Lowell
  • University of Houston
  • University of Oklahoma
  • Massachusetts Institute of Technology
  • IBM
  • NCCR Catalysis
  • Tohoku University
  • University of Michigan, Ann Arbor
  • Georgia Institute of Technology
  • ExxonMobil
  • United States Department of Energy
  • Lawrence Berkeley National Laboratory
  • Iowa State University
  • Pacific Northwest National Laboratory
  • Meta
  • Toyota Research Institute of North America
  • Tsinghua University

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

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. (Figure presented.)

Original languageEnglish
Pages (from-to)102-111
Number of pages10
JournalNature Catalysis
Volume9
Issue number2
DOIs
StatePublished - Feb 2026

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