Data driven optimization in catalysis refers to the use of computational techniques and data analytics to improve the efficiency and effectiveness of catalytic processes. By leveraging large datasets, machine learning models, and various algorithms, researchers can rapidly identify optimal conditions, predict catalyst performance, and design new catalytic materials with enhanced properties.
