The discovery of new catalytic materials is a multifaceted process that involves a combination of theoretical understanding, experimental testing, and increasingly, computational methods. Traditionally, the process has been empirical, involving the synthesis and testing of numerous materials to identify those with desirable catalytic properties. However, recent advances in computational chemistry and machine learning are revolutionizing the field by enabling the prediction of catalytic performance before materials are synthesized.
