AI and ML can optimize existing catalysts by analyzing data from numerous experiments to identify the best conditions for catalytic reactions. Techniques like Bayesian optimization can efficiently search the parameter space, reducing the number of experiments needed. Additionally, genetic algorithms can be used to evolve catalyst formulations towards optimal performance by iteratively selecting and combining the best candidates.
