Digital systems improve catalyst design by leveraging machine learning algorithms to predict the performance of catalyst materials. These algorithms analyze large datasets from previous experiments to identify patterns and correlations that human researchers might miss. Additionally, computational chemistry tools like density functional theory (DFT) can simulate the electronic structure of catalysts, providing insights into their behavior at the atomic level.
