The platform leverages Bayesian Optimization (BO), a machine learning-based approach, to optimize chemical reactions. BO is a probabilistic model-based method designed to efficiently identify the maximum (or minimum) of an unknown black-box function. It constructs a probability model of the function using carefully selected samples, which guides the search process by suggesting the next point to evaluate. The BO model incorporates both exploitation and exploration strategies. Exploitation involves investigating areas predicted to have the highest value, while exploration focuses on exploring points where the model has limited knowledge. This dual approach prevents the model from becoming trapped in local maxima or minima. The iterative process continues with the model being updated after each new evaluation of the function until a predetermined threshold is reached or a specific number of experiments have been conducted.