Computational methods such as density functional theory (DFT) and machine learning are revolutionizing the way we design and understand catalysts. These tools allow researchers to predict the behavior of catalytic systems, screen potential catalysts, and optimize reaction conditions without extensive experimental trials. This accelerates the discovery process and leads to more efficient and targeted innovations.
