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Self-Driving Laboratories for Chemistry and Materials Science

Gary TomDepartment of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, CanadaStefan P. SchmidDepartment of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 1, CH-8093 Zurich, SwitzerlandSterling G. BairdYang CaoAcceleration Consortium, 80 St. George St, Toronto, Ontario M5S 3H6, CanadaKourosh DarvishAcceleration Consortium, 80 St. George St, Toronto, Ontario M5S 3H6, CanadaHan HaoAcceleration Consortium, 80 St. George St, Toronto, Ontario M5S 3H6, CanadaStanley LoDepartment of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, CanadaSergio Pablo‐GarcíaDepartment of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, CanadaElla Miray RajaonsonDepartment of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, CanadaMarta SkretaVector Institute for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, CanadaNaruki YoshikawaVector Institute for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, CanadaSamantha CorapiDepartment of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, CanadaGun Deniz AkkocHelmholtz Institute Erlangen-NürnbergFelix Strieth‐KalthoffDepartment of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, CanadaMartin SeifridDepartment of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, CanadaAlán Aspuru‐GuzikDepartment of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, Ontario M5S 3E5, Canada

2024en

ABI

Annotatsiya

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

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DOI: 10.1021/acs.chemrev.4c00055

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