Automated pH Adjustment Driven by Robotic Workflows and Active Machine Learning

Pomberger, Alexander; Jose, Nicholas A.; Walz, D.; Meissner, Jens; Holze, C.; Kopczynski, Matthaeus; Müller-Bischof, Philipp; Lapkin, Alexei A.

doi:10.1016/j.cej.2022.139099

Cited by 11 publications

(16 citation statements)

References 29 publications

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“…Additionally, self-optimization has shown promise in the automated optimization of single reactions, but there is a wealth of reaction data available that current algorithms are unable to utilize. Very recent work has used transfer learning techniques to accelerate optimization by leveraging data from similar reaction optimization campaigns, but this has mainly been demonstrated in silico, , with one active learning example from Lapkin and co-workers with the focus of pH adjustment . There is also a significant opportunity for benchmarking these algorithms on both in silico and real-life experimental case studies to see how well they generalize to all classes of reactions.…”

Section: Self-optimizationmentioning

confidence: 99%

A Brief Introduction to Chemical Reaction Optimization

et al. 2023

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From the start of a synthetic chemist’s training, experiments are conducted based on recipes from textbooks and manuscripts that achieve clean reaction outcomes, allowing the scientist to develop practical skills and some chemical intuition. This procedure is often kept long into a researcher’s career, as new recipes are developed based on similar reaction protocols, and intuition-guided deviations are conducted through learning from failed experiments. However, when attempting to understand chemical systems of interest, it has been shown that model-based, algorithm-based, and miniaturized high-throughput techniques outperform human chemical intuition and achieve reaction optimization in a much more time- and material-efficient manner; this is covered in detail in this paper. As many synthetic chemists are not exposed to these techniques in undergraduate teaching, this leads to a disproportionate number of scientists that wish to optimize their reactions but are unable to use these methodologies or are simply unaware of their existence. This review highlights the basics, and the cutting-edge, of modern chemical reaction optimization as well as its relation to process scale-up and can thereby serve as a reference for inspired scientists for each of these techniques, detailing several of their respective applications.

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Section: Self-optimizationmentioning

confidence: 99%

A Brief Introduction to Chemical Reaction Optimization

et al. 2023

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“…Because the acquisition function paired with an optimization algorithm negates the need for humans to design the experiments inside the experiment-analysis-plan feedback loop, BO can orchestrate autonomous, "self-driving" labs [15][16][17][18][19][20][21][22] that employ automated instrumentation and/or robots to conduct a sequence of experiments with the goal of resourceefficient materials discovery and optimization.…”

Section: Bayesian Optimization For Materials Discoverymentioning

confidence: 99%

Multi-fidelity Bayesian optimization of covalent organic frameworks for xenon/krypton separations

Gantzler,

Deshwal,

Doppa

et al. 2023

Digital Discovery

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“…Stein et al 22 provided a conceptual summary visualizing different throughputs of acceleration setups on a materials-interfaces-systems scale. While setups developed for characterization of materials and interfaces can acquire data within seconds, 5,16,22,23 testing an assembled cell may take months. The denition of highthroughput, therefore, can change depending on the application.…”

Section: Odacell Setupmentioning

confidence: 99%