AI enhances catalyst design by using predictive modeling to forecast the performance of potential catalysts before they are synthesized. Techniques like neural networks and genetic algorithms can analyze large datasets to identify patterns and relationships between catalyst structures and their activities. This helps researchers to focus on the most promising candidates and improve the efficiency of the design process.
