Computational screening leverages advanced algorithms and molecular modeling techniques to predict the catalytic properties of materials before they are synthesized. This approach can significantly reduce the number of experimental trials needed, making the discovery process more efficient. Machine learning models can be trained on existing data to identify patterns and predict the performance of new catalysts.