How Does Machine Learning Predict Catalytic Activities?
ML models use historical data from previous experiments to learn the relationships between different variables and their impact on catalytic activity. Techniques such as regression analysis, neural networks, and support vector machines are commonly used. These models can predict the performance of new catalysts by extrapolating from the existing data, allowing researchers to make informed decisions about which catalysts to synthesize and test.
