Combining Bayesian optimization and automation to simultaneously optimize reaction conditions and routes

Schilter, Oliver; Gutierrez, Daniel Pacheco; Folkmann, Linnea M.; Castrogiovanni, Alessandro; García-Durán, Alberto; Zipoli, Federico; Roch, Loïc M.; Laino, Teodoro

doi:10.1039/d3sc05607d

Cited by 2 publications

(1 citation statement)

References 51 publications

(72 reference statements)

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“…445 While not optimizing for general reaction conditions, Schilter et al recently performed a simultaneous optimization over multiple substrates. 437 Using a robotic batch system containing six reactors, the authors performed a single optimization campaign in which the yield and conversion for an alkyne iodination was jointly optimized for multiple substrates. Notably, in the optimization campaign, the substrate was a parameter that could be chosen by the optimization algorithm to optimize the reaction conditions.…”

Section: Discrete and Categorical Optimization And Batch-type Reactorsmentioning

confidence: 99%

Self-Driving Laboratories for Chemistry and Materials Science

Tom,

Schmid,

Baird

et al. 2024

Preprint

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Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with the autonomization of experiment planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review article 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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Section: Discrete and Categorical Optimization And Batch-type Reactorsmentioning

confidence: 99%

Self-Driving Laboratories for Chemistry and Materials Science

Tom,

Schmid,

Baird

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Self-Driving Laboratories for Chemistry and Materials Science

Tom,

Schmid,

Baird

et al. 2024

Chem. Rev.

View full text Add to dashboard Cite

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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Combining Bayesian optimization and automation to simultaneously optimize reaction conditions and routes

Abstract: Combining a cloud-based Bayesian optimization platform with a robotic synthesis platform accelerated the discovery of high conversion iodination of terminal alkyne reactions in a large search space of over 12 000 possible reactions in 23 experiments.

Cited by 2 publications

References 51 publications

Self-Driving Laboratories for Chemistry and Materials Science

Self-Driving Laboratories for Chemistry and Materials Science

Self-Driving Laboratories for Chemistry and Materials Science

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