Aldulaijan, N., Marsden, J.A., Manson, J.A. et al. (1 more author) (2023) Adaptive Mixed Variable Bayesian Self-Optimisation of Catalytic Reactions. Reaction Chemistry and Engineering. ISSN 2058-9883
Abstract
Catalytic reactions play a central role in many industrial processes, owing to their ability to enhance efficiency and sustainability. However, complex interactions between the categorical and continuous variables leads to non-smooth response surfaces, which traditional optimisation methods struggle to navigate. Herein, we report the development and benchmarking of a new Adaptive Latent Bayesian Optimiser (ALaBO) algorithm for mixed variable chemical reactions. ALaBO was found to outperform other open-source Bayesian optimisation toolboxes, when applied to a series of test problems based on simulated kinetic data of catalytic reactions. Furthermore, through integration of ALaBO with a continuous flow reactor, we achieved the rapid self-optimisation of an exemplar Suzuki-Miyaura cross-coupling reaction involving six distinct ligands, identifying a 93% yield within a budget of just 25 experiments.
Metadata
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Institution: | The University of Leeds | ||||
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds) | ||||
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Depositing User: | Symplectic Publications | ||||
Date Deposited: | 18 Oct 2023 08:43 | ||||
Last Modified: | 18 Oct 2023 08:43 | ||||
Status: | Published online | ||||
Publisher: | Royal Society of Chemistry | ||||
Identification Number: | https://doi.org/10.1039/D3RE00476G |