Issue 2, 2024

Adaptive mixed variable Bayesian self-optimisation of catalytic reactions

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.

Graphical abstract: Adaptive mixed variable Bayesian self-optimisation of catalytic reactions

Supplementary files

Article information

Article type
Paper
Submitted
11 Sept. 2023
Accepted
14 Okt. 2023
First published
17 Okt. 2023
This article is Open Access
Creative Commons BY license

React. Chem. Eng., 2024,9, 308-316

Adaptive mixed variable Bayesian self-optimisation of catalytic reactions

N. Aldulaijan, J. A. Marsden, J. A. Manson and A. D. Clayton, React. Chem. Eng., 2024, 9, 308 DOI: 10.1039/D3RE00476G

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