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<p>Catalysis is essential to modern chemical manufacturing and environmental sustainability. Yet, traditional catalyst discovery remains slow, resource-intensive and constrained by human-centred trial-and-error workflows. The integration of artificial intelligence (AI), robotics and high-throughput experimentation into self-driving laboratories (SDLs) presents a transformative approach for accelerating catalyst discovery and optimization. SDLs combine automated synthesis and testing platforms, data infrastructures and AI-guided decision-making to enable information-rich experimentation and the fast-tracked generation of scientific knowledge. However, in our view, realizing the full potential of SDLs requires sustained human oversight to ensure rigorous data curation, validate machine-generated hypotheses and establish benchmarks to mitigate AI-related errors. This Perspective outlines core SDL components, including hardware, computational modelling and AI-guided decision-making. We discuss challenges in data availability, integration of computational and experimental workflows and scalable platforms. Finally, we outline immediate opportunities to broaden the adoption of autonomous experimentation in catalysis.</p><p></p>

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Autonomous catalysis research with human–AI–robot collaboration

  • 期刊论文

  • 发表日期：

    2025年11月5日

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  • Negin Orouji,
  • Jeffrey A. Bennett,
  • Richard B. Canty,
  • Long Qi,
  • Shijing Sun,
  • Paulami Majumdar,
  • Chong Liu,
  • Núria López,
  • Neil M. Schweitzer,
  • John R. Kitchin,
  • Hongliang Xin,
  • Milad Abolhasani

摘要

Catalysis is essential to modern chemical manufacturing and environmental sustainability. Yet, traditional catalyst discovery remains slow, resource-intensive and constrained by human-centred trial-and-error workflows. The integration of artificial intelligence (AI), robotics and high-throughput experimentation into self-driving laboratories (SDLs) presents a transformative approach for accelerating catalyst discovery and optimization. SDLs combine automated synthesis and testing platforms, data infrastructures and AI-guided decision-making to enable information-rich experimentation and the fast-tracked generation of scientific knowledge. However, in our view, realizing the full potential of SDLs requires sustained human oversight to ensure rigorous data curation, validate machine-generated hypotheses and establish benchmarks to mitigate AI-related errors. This Perspective outlines core SDL components, including hardware, computational modelling and AI-guided decision-making. We discuss challenges in data availability, integration of computational and experimental workflows and scalable platforms. Finally, we outline immediate opportunities to broaden the adoption of autonomous experimentation in catalysis.