Figure outlines the basic steps for the optimization of chemical reactions using ML methods. The workflow requires an initial set of experimental data that contains different variables for reaction conditions (i.e. temperature, time, solvent, catalyst, etc.) and the corresponding outcome values for the target optimization objectives (e.g. yield, purity, cost, etc.). The initial data set is commonly obtained by sampling a combination of reaction variables from the parametric space, performing the synthesis experiments under the selected reaction conditions, and measuring the values for the target optimization objectives. The sampling of initial reaction variables often is performed through near-random statistical methods such as Latin hypercube sampling (LHS), Sobol sampling, full factorial sampling, and centre point sampling methods. Alternatively, the initial data set can be obtained from values previously reported in the literature. After, one or various predictive models are fitted to the initial data set to predict the expected values of the optimization objectives, the number of models that are fitted depends on the number of optimization objectives, and normally one model is constructed for each optimization objective. The next step involves the application of an optimization algorithm to find the most likely parameters that would lead to optimal outcomes for the target optimization objectives. Finally, a set of the most promising suggestions is selected and tested experimentally. The data set is then updated with the outcomes of the latest experimental parameters and the process is repeated until the optimal conditions have been found. Depending on the number of objectives, optimization campaigns are classified as single-objective (Figure ) or multi-objective optimizations (Figure ). In single-objective optimizations, the algorithm will explore the parametric space to find the optimal conditions by finding the variables that either maximize or minimize the target objective function. In multiobjective optimization, the algorithms will search for optimal conditions that either maximize or minimize each objective. On the other hand, when competing objectives are optimized the algorithm aims to discover the set of solutions where the improvement in one of the objectives results in the deterioration of the other objective. This set of solutions is called the Pareto front of the system (also known as nondominated solutions), all other solutions that are not part of the Pareto front are not optimal for any of the objectives and are referred to as dominated solutions. Since all solutions in the Pareto front are optimal the users hold the responsibility to choose the set of conditions for their specific application. The first reports of the application of ML to the optimization of chemical reactions can be traced over 20 years ago. A handful of studies used ML algorithms such as neural networks and support vector machines to fit models to chemical reaction data that then were optimized by a genetic algorithms . However, the use of ML for chemical reaction optimization did not popularize until the introduction of Bayesian Optimization (BO) techniques by Lapkin and Bourne's research group . BO is a global optimization method that fits a probabilistic function to model the optimization objective and utilizes it to search for parameters that will likely lead to optimal objective values.