A machine learning-enabled process optimization of ultra-fast flow chemistry with multiple reaction metrics

Karan, Dogancan; Chen, Guoying; Jose, Nicholas; Bai, Jiaru; McDaid, Paul; Lapkin, Alexei A.

doi:10.1039/d3re00539a

Cited by 6 publications

(5 citation statements)

References 41 publications

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“…Additionally, the integration of artificial intelligence for autonomous flow chemistry and machine learning is expected to contribute to further advancements in this field. 128–130 Consequently, this technology is posed to play a pivotal role in the evolution of chemical processes, contributing to the synthesis of new therapeutic agents and valuable chemical intermediates.…”

Section: Conclusion and Outlooksmentioning

confidence: 99%

Continuous flow reactions in the preparation of active pharmaceutical ingredients and fine chemicals

Martins,

Braga,

de Castro

et al. 2024

Chem. Commun.

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Section: Conclusion and Outlooksmentioning

confidence: 99%

Continuous flow reactions in the preparation of active pharmaceutical ingredients and fine chemicals

Martins,

Braga,

de Castro

et al. 2024

Chem. Commun.

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show abstract

“…Similarly, Karan et al also employed the TS-EMO algorithm for the Pareto optimization of the yield and impurity for an ultra-fast lithium-halogen exchange reaction. 435 The authors performed three optimization campaigns with either different initial experiments or different reactant mixing equipment, showing that the algorithm efficiently converges to similar Pareto fronts.…”

Section: Pareto Optimizations and Further Algorithmic Advancesmentioning

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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“…In recent years, the application of machine learning, particularly Bayesian optimization, has significantly advanced the optimization of continuous flow reactions. − This method enables the rapid identification of optimal reaction conditions with a minimal number of experiments. − For instance, Fuse et al utilized Bayesian optimization to efficiently navigate a search space of 10,500 potential reaction condition combinations, successfully identifying the desired conditions for unsymmetrical sulfamide synthesis in just 29 experiments . The efficiency of Bayesian optimization is notably enhanced when integrated with automated platforms.…”

Section: Introductionmentioning

confidence: 99%

Continuous Flow Synthesis of N,O-Dimethyl-N′-nitroisourea Monitored by Inline Fourier Transform Infrared Spectroscopy: Bayesian Optimization and Kinetic Modeling

Guo,

Luo,

Chai

et al. 2024

Ind. Eng. Chem. Res.

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The synthesis of N,O-dimethyl-N′-nitroisourea, crucial intermediates in pesticide manufacturing, was explored through a substitution reaction between O-methyl-N-nitroisourea and methylamine within a novel continuous flow microreactor system, featuring Fourier transform infrared (FTIR) in-line analysis for real-time monitoring. In this paper, the reaction is investigated using two optimization methods: the contemporary machine learning-based Bayesian optimization and the traditional kinetic modeling. Remarkably, both strategies obtained a similar yield of approximately 83% under equivalent reaction parametersspecifically, an initial reactant concentration of 0.2 mol/L, a reaction temperature of 40 °C, a molar ratio of reactants at 5:1, and a residence time of 240 min. The Bayesian optimization method demonstrated a notable efficiency, achieving optimal conditions within a mere 20 experiments, in contrast to the kinetic modeling approach, which required a more laborious effort for model formulation and validation. However, kinetic modeling allows for a more comprehensive understanding of the reaction, and the two optimization methods fully demonstrate their respective strengths and weaknesses. This study not only highlights the potential of integrating advanced machine learning methods into chemical process optimization but also sets the stage for further exploration into efficient, data-driven approaches in chemical synthesis.

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A machine learning-enabled process optimization of ultra-fast flow chemistry with multiple reaction metrics

Abstract: An automated flow chemistry platform was designed to collect data for a lithium-halogen exchange reaction. The data was used to train a Bayesian multi-objective optimization algorithm to optimize the process parameters and build process knowledge.

Cited by 6 publications

References 41 publications

Continuous flow reactions in the preparation of active pharmaceutical ingredients and fine chemicals

Continuous flow reactions in the preparation of active pharmaceutical ingredients and fine chemicals

Self-Driving Laboratories for Chemistry and Materials Science

Continuous Flow Synthesis of N,O-Dimethyl-N′-nitroisourea Monitored by Inline Fourier Transform Infrared Spectroscopy: Bayesian Optimization and Kinetic Modeling

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