These algorithms work by analyzing vast amounts of data from previous experiments and simulations to identify patterns and correlations. For instance, machine learning models can be trained on datasets containing information about different catalysts, reaction conditions, and outcomes. Once trained, these models can predict the performance of new catalysts under various conditions.
