The design of new catalysts can be significantly enhanced through digitalization. Advanced computational techniques, such as density functional theory (DFT) and molecular dynamics simulations, allow researchers to model and predict the behavior of catalysts at the atomic level. Additionally, machine learning algorithms can analyze vast datasets to identify patterns and suggest novel catalytic materials that may not be immediately obvious through traditional methods.
