工作流程
自动化
化学
纳米技术
科学发现
领域(数学分析)
数据科学
计算机科学
工程类
机械工程
心理学
数学分析
材料科学
数学
数据库
认知科学
作者
Gary Tom,Stefan P. Schmid,Sterling G. Baird,Yang Cao,Kourosh Darvish,Han Hao,Stanley Lo,Sergio Pablo‐García,Ella Miray Rajaonson,Marta Skreta,Naruki Yoshikawa,Samantha Corapi,Gun Deniz Akkoc,Felix Strieth‐Kalthoff,Martin Seifrid,Alán Aspuru‐Guzik
出处
期刊:Chemical Reviews
[American Chemical Society]
日期:2024-08-13
卷期号:124 (16): 9633-9732
被引量:348
标识
DOI:10.1021/acs.chemrev.4c00055
摘要
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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