ML and AI algorithms can process vast amounts of experimental and computational data to identify patterns and correlations that are not readily apparent. This capability allows for the design of new catalytic materials with desired properties. For instance, predictive models can be trained to forecast the activity, selectivity, and stability of catalysts, significantly reducing the time and cost associated with experimental trials.
