A Multi-Objective Active Learning Platform and Web App for Reaction Optimization
Humans
Bayes Theorem
Mobile Applications
01 natural sciences
Software
0104 chemical sciences
DOI:
10.26434/chemrxiv-2022-cljcp
Publication Date:
2022-08-17T03:57:00Z
AUTHORS (9)
ABSTRACT
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 datasets 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-word test scenarios for the simultaneous optimization of 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 1,728 possible configurations available in each optimization. To make the platform more accessible to non-experts, we developed a Graphical User Interface (GUI) that can be accessed online through a web-based application and incorporated features such as conditions 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.
SUPPLEMENTAL MATERIAL
Coming soon ....
REFERENCES (0)
CITATIONS (0)
EXTERNAL LINKS
PlumX Metrics
RECOMMENDATIONS
FAIR ASSESSMENT
Coming soon ....
JUPYTER LAB
Coming soon ....