Machine learning techniques are widely used in predictive modeling for catalysis. These techniques can analyze large datasets to identify patterns and correlations that are not evident through traditional methods. Algorithms such as neural networks, decision trees, and support vector machines can be trained to predict catalytic activity and selectivity with high accuracy.
