Machine learning (ML) algorithms can analyze vast datasets to identify patterns and correlations that may not be apparent through traditional analysis. In catalysis, ML can predict the activity and stability of catalysts, optimize reaction conditions, and even suggest new catalyst designs. Techniques such as neural networks, support vector machines, and decision trees are commonly used.
