Accelerated Chemical Reaction Optimization with Active Learning to Build your Reaction Digital Twin

The optimization of chemical reactions is very often performed using a trial-and-error methodology and modifying one experimental variable at a time. In this presentation, we will show how the active learning methodology is able to reduce the number of experiments required to optimize chemical reactions by building a “digital twin” of the chemical reaction system.

We will present one of the most well-known active learning algorithms: Bayesian optimization. This algorithms combines two strategies (exploration and exploitation) to build a surrogate machine learning model of the chemical reaction experiment-by-experiment. This surrogate model, the “digital twin” of our chemical reaction system, is combined with an acquisition function in order to iteratively and quickly find an optimal combination of reaction parameters.

We will discuss the advantages and challenges of optimizing chemical reactions using Bayesian optimization algorithms and compare this machine learning-based approach with the traditional Design of Experiments (DoE) methodology.