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<p>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.</p><p></p>

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Roadmap for transforming heterogeneous catalysis with artificial intelligence

  • 期刊论文

  • 发表日期：

    2026年2月16日

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  • 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

摘要

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.