A Multi-Objective Active Learning Platform and Web App for Reaction Optimization

We report the development of an open-source experimental design via Bayesian optimization platform for multiimentation (HTE) and virtual screening data sets containing highdimensional 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 crosselectrophile 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.

Automated summary

We developed an open-source experimental design platform based on Bayesian optimization algorithm for multiimentation (HTE) and virtual screening data sets. The platform optimized the reaction yield and enantioselectivity in a short time period, surpassing human-driven optimization. To make it more accessible to nonexperts, a graphical user interface with features like real-time condition modification and data visualization was developed.