Machine learning meets continuous flow chemistry: Automated optimization towards the Pareto front of multiple objectives

Schweidtmann, Artur M.; Clayton, Adam D.; Holmes, Nicholas P.; Bradford, Eric; Bourne, Richard A.

doi:10.1016/j.cej.2018.07.031

Cited by 274 publications

(255 citation statements)

References 45 publications

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“…With a suitable system for the automated optimisation of LLEs in hand, our attention shifted to the simultaneous optimisation of a tandem reaction and extraction process. For this, the synthesis and purification of N-benzyl-α-methylbenzylamine 2 in continuous flow was investigated (Scheme 2) [7]. As previously, aqueous nitric acid was introduced into the CSTR mixing module for the removal of amine-containing impurities.…”

Section: Tandem Reaction-extraction Optimisationmentioning

confidence: 99%

“…This reduces the amount of time a researcher spends conducting repetitive routine experimentation, allowing them to focus on the more challenging aspects of chemical discovery and development. Many improvements have been reported in this area over the past decade, predominantly focusing on optimisation algorithms [7][8][9] and different analytical techniques [10][11][12]. However, these reports have been limited to low complexity optimisations of single step reactions.…”

Section: Introductionmentioning

confidence: 99%

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Self-optimising reactive extractions: towards the efficient development of multi-step continuous flow processes

et al. 2020

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Downstream purification of products and intermediates is essential for the development of continuous flow processes. Described herein, is a study on the use of a modular and reconfigurable continuous flow platform for the self-optimisation of reactive extractions and multi-step reaction-extraction processes. The selective extraction of one amine from a mixture of two similar amines was achieved with an optimum separation of 90%, and in this case, the black-box optimisation approach was superior to global polynomial modelling. Furthermore, this methodology was utilised to simultaneously optimise the continuous flow synthesis and work-up of N-benzyl-α-methylbenzylamine with respect to four variables, resulting in a significantly improved purity.

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Section: Tandem Reaction-extraction Optimisationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Self-optimising reactive extractions: towards the efficient development of multi-step continuous flow processes

et al. 2020

Self Cite

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“…13) Thermi-Abb. 1. a) Zuführung der Photokatalysatorsuspension zur Gas-Flüssig-Mischung; 4) b) Kohlenstoffnitrid (gelb) als fester Photokatalysator in den seriellen Mikro-Batch-Reaktoren; 4) 22) Das Massachusetts Institute of Technology stellte ein neues Gerätekonzept vor, in dem sich durch Plug-and-play-Einsatz verschiedener Reaktortypen -etwa geheizt, gekühlt, packed-bed, photochemischfluidische Trennmodule, Prozesssensorik, -analytik und -steuerung viele zum Teil zweistufige Reaktionen (gegebenenfalls ferngesteuert) durchführen und automatisch optimieren lassen (Abbildung 4). 23) Die autonome Selbstoptimierung eines sogar vierstufigen Reaktionsprozesses zeigten erstmals Felpin und Mitarbeiter, und zwar an der kontinuierlichen Naturstoffsynthese von Carpanon.…”

Section: Prozessanalyseunclassified

Trendbericht Technische Chemie

Deutschmann¹,

Dittmeyer²,

Grunwaldt³

et al. 2019

Nachr Chem

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“…Statistical methods were previously introduced to analyze the vast quantities of data generated by laboratory robots (6,7), and recently, machine learning algorithms have started to be integrated into laboratory equipment (8,9). However, most of these methods focus on the optimization of targeted properties (10,11) or require previous knowledge (12,13).…”

Section: Introductionmentioning

confidence: 99%

A curious formulation robot enables the discovery of a novel protocell behavior

et al. 2020

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We describe a chemical robotic assistant equipped with a curiosity algorithm (CA) that can efficiently explore the states a complex chemical system can exhibit. The CA-robot is designed to explore formulations in an open-ended way with no explicit optimization target. By applying the CA-robot to the study of self-propelling multicomponent oil-in-water protocell droplets, we are able to observe an order of magnitude more variety in droplet behaviors than possible with a random parameter search and given the same budget. We demonstrate that the CA-robot enabled the observation of a sudden and highly specific response of droplets to slight temperature changes. Six modes of self-propelled droplet motion were identified and classified using a time-temperature phase diagram and probed using a variety of techniques including NMR. This work illustrates how CAs can make better use of a limited experimental budget and significantly increase the rate of unpredictable observations, leading to new discoveries with potential applications in formulation chemistry.

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Machine learning meets continuous flow chemistry: Automated optimization towards the Pareto front of multiple objectives

Cited by 274 publications

References 45 publications

Self-optimising reactive extractions: towards the efficient development of multi-step continuous flow processes

Self-optimising reactive extractions: towards the efficient development of multi-step continuous flow processes

Trendbericht Technische Chemie

A curious formulation robot enables the discovery of a novel protocell behavior

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