Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis

Amar, Yehia; Schweidtmann, Artur M.; Deutsch, Paul; Cao, Liwei; Lapkin, Alexei A.

doi:10.1039/c9sc01844a

Cited by 109 publications

(108 citation statements)

References 60 publications

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“…With respect to the handling of discrete variables, such as reagents and solvents, the TS-EMO optimizing algorithm was recently reported to be successful in optimizing for solvents in a ruthenium-catalyzed asymmetric hydrogenation reaction. [10] Developments into handling discrete variables are currently underway in our laboratory, with the aim to demonstrate the improved capabilities and efficiencies using robotic workflows in process development.…”

Section: Discussionmentioning

confidence: 99%

“…An example of an algorithm for efficient multi‐objective reaction optimization is the open‐source Thompson Sampling Efficient Multi‐Objective (TS‐EMO) [7] . Lapkin and co‐workers [6,8–10] have demonstrated the quality of the generated Pareto fronts, as well as the algorithm's efficiency at identifying them, when compared with alternative algorithms such as ParEGO [11] . Alternative examples multi‐objective algorithms [12] developed for chemical process include Phoenics [13] and Chimera [14] …”

Section: Introductionmentioning

confidence: 99%

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A Machine Learning‐Enabled Autonomous Flow Chemistry Platform for Process Optimization of Multiple Reaction Metrics

2020

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Self-optimization of chemical reactions using machine learning multi-objective algorithms has the potential to significantly shorten overall process development time, providing users with valuable information about economic and environmental factors. Using the Thompson Sampling Efficient Multi-Objective (TS-EMO) algorithm, the self-optimization flow chemistry system in this report demonstrates the ability to identify optimum reaction conditions and trade-offs (Pareto fronts) between conflicting optimization objectives, such as yield, cost, spacetime yield, and E-factor, in a data efficient manner. Advantageously, the robust system consists of exclusively commercially available equipment and a user-friendly MATLAB graphical user interface, and was shown to autonomously run 131 experiments over 69 hours uninterrupted.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Machine Learning‐Enabled Autonomous Flow Chemistry Platform for Process Optimization of Multiple Reaction Metrics

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…[62][63][64][65][66][67][68] In other areas, including organic synthesis, [69][70][71][72][73] and theoretical [74][75][76][77][78][79][80][81] and inorganic 82,83 chemistry, the use of ML is rapidly growing. In catalysis, 84,85 several examples have been reported for both heterogeneous [86][87][88][89][90][91][92][93] and homogeneous [94][95][96][97][98] systems.…”

Section: Introductionmentioning

confidence: 99%

Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex

et al. 2020

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“…A binary input is set to 1 if the respective integer is selected and is 0 otherwise (so-called SOS-1 set). It should be noted that the consideration of discrete decision variables in surrogatebased optimization was applied in the literature (e.g., [43][44][45]), but theoretical foundations of these methods are an active field of research [46,47].…”

Section: Mechanistic Process Model To Describe the Process Plantmentioning

confidence: 99%

Simultaneous rational design of ion separation membranes and processes

Rall

Schweidtmann

Aumeier

et al. 2020

Journal of Membrane Science

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Economically viable water treatment process plants for drinking water purification are a prerequisite for sustainable supply of safe drinking water in the future. However, modern membrane process development experiences a disconnect in this domain: the synthesis of the membrane and the design of the process are decoupled. We propose an optimization strategy to simultaneously design the performance of layer-by-layer nanofiltration membrane modules and the separation process. This approach achieves overall optimal performance by extending the search space and thus exploiting synergies. Better separation performances at a lower cost as compared to conventional optimization strategies can be achieved. The key feature of this optimization framework is the integration of artificial neural networks. This machine-learning technique describes membrane performance as a function of its synthesis protocol. We optimize the design problem rigorously by a deterministic global nonlinear optimization method. Thus, this framework yields membrane synthesis protocols and membrane processes that are optimally tailored to the desired separation task. In a showcase, the simultaneous membrane synthesis and process optimization design achieve immediately favorable results with lower impurities at comparable costs. The process investment and operation costs are compared to a state of the art commercially available membrane for nanofiltration.

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Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis

Abstract: Rational solvent selection remains a significant challenge in process development.

Cited by 109 publications

References 60 publications

A Machine Learning‐Enabled Autonomous Flow Chemistry Platform for Process Optimization of Multiple Reaction Metrics

A Machine Learning‐Enabled Autonomous Flow Chemistry Platform for Process Optimization of Multiple Reaction Metrics

Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex

Simultaneous rational design of ion separation membranes and processes

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