How Do Computational Tools Help in Catalyst Design?
The advent of computational chemistry and machine learning has revolutionized catalyst design. These tools allow scientists to model and predict the behavior of catalysts under various conditions, thereby reducing the need for extensive experimental trials. Techniques such as Density Functional Theory (DFT) and molecular dynamics simulations are particularly useful in understanding the electronic properties and dynamics of catalysts.
