2022
DOI: 10.1039/d2sc04419f
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The way to AI-controlled synthesis: how far do we need to go?

Abstract: Chemical synthesis always plays an irreplaceable role in the chemical, material, and pharmacological fields. Meanwhile, artificial intelligence (AI) is causing a rapid technological revolution in the field of replacing manual...

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Cited by 5 publications
(2 citation statements)
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“…Besides the use of human intervention to rene the automated continuous-ow platforms, algorithm-driven optimization has gained signicant attention in both academia and industry as a way to explore high-dimensional chemical space and achieve optimal conditions with fewer experiments. 107,[130][131][132][133][134][135][136] There are three main types of algorithms applied for selfoptimization experimentation: local optimization algorithms such as design of experiments (DoE) 137,138 and Nelder-Mead simplex, 139,140 global optimization algorithms like SNOBFIT 141,142 and Bayesian Optimizations, 114,130,[143][144][145] and machine learning algorithms like deep reinforcement learning. 146 Jensen et al are pioneers in this growing eld, having developed various versions of automated continuous-ow platforms, including a fridge-size recongurable platform, 147 a 'plug-and-play' platform, 142 and a robotic platform.…”
Section: High-throughput Experimentation and Automationmentioning
confidence: 99%
“…Besides the use of human intervention to rene the automated continuous-ow platforms, algorithm-driven optimization has gained signicant attention in both academia and industry as a way to explore high-dimensional chemical space and achieve optimal conditions with fewer experiments. 107,[130][131][132][133][134][135][136] There are three main types of algorithms applied for selfoptimization experimentation: local optimization algorithms such as design of experiments (DoE) 137,138 and Nelder-Mead simplex, 139,140 global optimization algorithms like SNOBFIT 141,142 and Bayesian Optimizations, 114,130,[143][144][145] and machine learning algorithms like deep reinforcement learning. 146 Jensen et al are pioneers in this growing eld, having developed various versions of automated continuous-ow platforms, including a fridge-size recongurable platform, 147 a 'plug-and-play' platform, 142 and a robotic platform.…”
Section: High-throughput Experimentation and Automationmentioning
confidence: 99%
“…39,40 The field of reactor technology is rapidly advancing, driven by developments in data processing, which encompass the utilisation of recently matured data science disciplines, [41][42][43][44] machine learning, 45,46 and sophisticated artificial intelligence methods. [47][48][49] This inflicts various discussed research areas such as microfluidics, [50][51][52] enzyme development, [53][54][55][56][57] or reaction optimisation. [58][59][60] The implementation of these methods underlines the growing necessity for reliable, rapid raw data generation and instrumental hardware control, preferably within continuous flow systems integrated with real-time online analysis.…”
Section: Introductionmentioning
confidence: 99%