Machine learning models in catalysis work by training algorithms on large datasets of known catalytic reactions. These datasets may include various features such as catalyst composition, reaction conditions, and observed outcomes. Once trained, the algorithms can predict the behavior of new catalytic systems. Popular techniques include neural networks, decision trees, and support vector machines.
