AI can dramatically improve catalyst design by analyzing large datasets to identify patterns and correlations that human researchers might miss. For example, machine learning models can predict the activity, selectivity, and stability of potential catalysts based on their properties and past performance. This can lead to the discovery of novel catalysts that are more efficient or selective than existing ones.