ML algorithms can streamline the catalyst design process by predicting the properties of new materials before they are synthesized. For instance, neural networks can model the relationship between the molecular structure of a catalyst and its activity. This allows researchers to screen a vast number of potential catalysts computationally, significantly reducing the experimental workload.