Machine learning (ML) is revolutionizing the field of catalysis by enabling the analysis of large datasets to identify patterns and make predictions. ML algorithms can be trained on experimental and computational data to predict the performance of new catalysts, optimize reaction conditions, and even discover novel catalytic materials. Techniques such as neural networks and regression models are increasingly being used to enhance the efficiency and accuracy of catalytic research.
