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<p>Self-driving laboratories have the potential to revolutionize chemical discovery and optimization, yet their widespread adoption remains limited by high costs, complex infrastructure and limited accessibility. Here we introduce RoboChem-Flex, a low-cost, modular self-driving laboratory platform designed to democratize autonomous chemical experimentation. The system combines customizable, in-house-built hardware with a flexible Python-based software framework that integrates real-time device control and advanced Bayesian optimization strategies, including multi-objective and transfer learning workflows. RoboChem-Flex supports both fully autonomous closed-loop operation and human-in-the-loop configurations, enabling seamless integration with shared analytical equipment and minimizing entry barriers. We validate the versatility of the platform across six diverse case studies, including photocatalysis, biocatalysis, thermal cross-couplings and enantioselective catalysis, spanning both single- and multi-objective optimizations. Through these case studies, we demonstrate RoboChem-Flex’s ability to navigate large, complex chemical spaces, autonomously identify scalable high-performance reaction conditions, and flexibly adapt to a variety of analytical set-ups. By providing an affordable, scalable and open platform, RoboChem-Flex offers a tangible step towards making self-driving laboratories accessible to resource-limited laboratories, fostering broader participation in automated chemical research.</p><p></p>

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A flexible and affordable self-driving laboratory for automated reaction optimization

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月13日

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  • Simone Pilon,
  • Elia Savino,
  • Oliver M. Bayley,
  • Michael Vanzella,
  • Miguel Claros,
  • Petros Siasiaridis,
  • Junsong Liu,
  • Florian Lukas,
  • Matteo Damian,
  • Vasilis Tseliou,
  • Niccolò Intini,
  • Aidan Slattery,
  • Jesus SanJosé-Orduna,
  • Tim den Hartog,
  • Ron A. H. Peters,
  • Andrea F. G. Gargano,
  • Francesco G. Mutti,
  • Timothy Noël

摘要

Self-driving laboratories have the potential to revolutionize chemical discovery and optimization, yet their widespread adoption remains limited by high costs, complex infrastructure and limited accessibility. Here we introduce RoboChem-Flex, a low-cost, modular self-driving laboratory platform designed to democratize autonomous chemical experimentation. The system combines customizable, in-house-built hardware with a flexible Python-based software framework that integrates real-time device control and advanced Bayesian optimization strategies, including multi-objective and transfer learning workflows. RoboChem-Flex supports both fully autonomous closed-loop operation and human-in-the-loop configurations, enabling seamless integration with shared analytical equipment and minimizing entry barriers. We validate the versatility of the platform across six diverse case studies, including photocatalysis, biocatalysis, thermal cross-couplings and enantioselective catalysis, spanning both single- and multi-objective optimizations. Through these case studies, we demonstrate RoboChem-Flex’s ability to navigate large, complex chemical spaces, autonomously identify scalable high-performance reaction conditions, and flexibly adapt to a variety of analytical set-ups. By providing an affordable, scalable and open platform, RoboChem-Flex offers a tangible step towards making self-driving laboratories accessible to resource-limited laboratories, fostering broader participation in automated chemical research.