Tom Gary, Schmid Stefan P, Baird Sterling G, Cao Yang, Darvish Kourosh, Hao Han, Lo Stanley, Pablo-García Sergio, Rajaonson Ella M, Skreta Marta, Yoshikawa Naruki, Corapi Samantha, Akkoc Gun Deniz, Strieth-Kalthoff Felix, Seifrid Martin, Aspuru-Guzik Alán
Department of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada.
Department of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada.
Chem Rev. 2024 Aug 28;124(16):9633-9732. doi: 10.1021/acs.chemrev.4c00055. Epub 2024 Aug 13.
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.
自动驾驶实验室(SDLs)有望加速科学方法的应用。通过实验工作流程的自动化以及自主实验规划,SDLs有潜力极大地加速化学和材料发现方面的研究。本综述深入分析了SDL技术的最新进展、其在各科学学科中的应用以及对研究和产业的潜在影响。此外,本综述还概述了SDLs的使能技术,包括其硬件、软件以及与实验室基础设施的集成。最重要的是,本综述探讨了SDLs做出重大贡献的各种科学领域,从药物发现、材料科学到基因组学和化学。我们全面回顾了SDLs的现有实际例子、它们不同程度的自动化以及与每个领域相关的挑战和局限性。