AI techniques such as machine learning and neural networks are increasingly being used to analyze large datasets generated from catalytic experiments. These technologies can identify patterns and correlations that are not easily discernible through traditional methods. For instance, AI can predict the activity and stability of a catalyst under various conditions, thus saving time and resources in the experimental phase.
