Issue 30, 2023

Continuous flow synthesis of pyridinium salts accelerated by multi-objective Bayesian optimization with active learning

Abstract

We report a human-in-the-loop implementation of the multi-objective experimental design via a Bayesian optimization platform (EDBO+) towards the optimization of butylpyridinium bromide synthesis under continuous flow conditions. The algorithm simultaneously optimized reaction yield and production rate (or space-time yield) and generated a well defined Pareto front. The versatility of EDBO+ was demonstrated by expanding the reaction space mid-campaign by increasing the upper temperature limit. Incorporation of continuous flow techniques enabled improved control over reaction parameters compared to common batch chemistry processes, while providing a route towards future automated syntheses and improved scalability. To that end, we applied the open-source Python module, nmrglue, for semi-automated nuclear magnetic resonance (NMR) spectroscopy analysis, and compared the acquired outputs against those obtained through manual processing methods from spectra collected on both low-field (60 MHz) and high-field (400 MHz) NMR spectrometers. The EDBO+ based model was retrained with these four different datasets and the resulting Pareto front predictions provided insight into the effect of data analysis on model predictions. Finally, quaternization of poly(4-vinylpyridine) with bromobutane illustrated the extension of continuous flow chemistry to synthesize functional materials.

Graphical abstract: Continuous flow synthesis of pyridinium salts accelerated by multi-objective Bayesian optimization with active learning

Supplementary files

Article information

Article type
Edge Article
Submitted
10 mar. 2023
Accepted
19 jun. 2023
First published
12 jul. 2023
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2023,14, 8061-8069

Continuous flow synthesis of pyridinium salts accelerated by multi-objective Bayesian optimization with active learning

J. H. Dunlap, J. G. Ethier, A. A. Putnam-Neeb, S. Iyer, S. L. Luo, H. Feng, J. A. Garrido Torres, A. G. Doyle, T. M. Swager, R. A. Vaia, P. Mirau, C. A. Crouse and L. A. Baldwin, Chem. Sci., 2023, 14, 8061 DOI: 10.1039/D3SC01303K

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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