How has computational modeling impacted catalyst design?
Computational modeling has revolutionized catalyst design by allowing researchers to predict the behavior of catalysts at the atomic level. Techniques like density functional theory (DFT) and molecular dynamics simulations enable the screening of potential catalyst materials before experimental testing. This accelerates the discovery process and reduces costs. Additionally, machine learning algorithms are increasingly being used to analyze vast datasets, leading to the identification of novel catalysts with desirable properties.
