We report the development of an open-source experimental design via Bayesian optimization platform for multi-objective reaction optimization. Using high-throughput experimentation (HTE) and virtual screening data sets containing high-dimensional continuous and discrete variables, we optimized the performance of the platform by fine-tuning the algorithm components such as reaction encodings, surrogate model parameters, and initialization techniques. Having established the framework, we applied the optimizer to real-world test scenarios for the simultaneous optimization of the reaction yield and enantioselectivity in a Ni/photoredox-catalyzed enantioselective cross-electrophile coupling of styrene oxide with two different aryl iodide substrates. Starting with no previous experimental data, the Bayesian optimizer identified reaction conditions that surpassed the previously human-driven optimization campaigns within 15 and 24 experiments, for each substrate, among 1728 possible configurations available in each optimization. To make the platform more accessible to nonexperts, we developed a graphical user interface (GUI) that can be accessed online through a web-based application and incorporated features such as condition modification on the fly and data visualization. This web application does not require software installation, removing any programming barrier to use the platform, which enables chemists to integrate Bayesian optimization routines into their everyday laboratory practices.
@article{torres2022multi,
year = {2022},
title = {A multi-objective active learning platform and web app for reaction optimization},
author = {Torres, Jose Antonio Garrido and Lau, Sii Hong and Anchuri, Pranay and Stevens, Jason M and Tabora, Jose E and Li, Jun and Borovika, Alina and Adams, Ryan P and Doyle, Abigail G},
journal = {Journal of the American Chemical Society},
volume = {144},
number = {43},
pages = {19999--20007},
publisher = {ACS Publications}
}