The main components of predictive models in catalysis include:
1. Data Collection: Gathering experimental and theoretical data on catalyst properties and reaction outcomes. 2. Model Building: Using statistical, machine learning, or quantum mechanical methods to create models that describe the catalytic process. 3. Validation: Comparing model predictions with experimental results to assess accuracy. 4. Optimization: Refining models to improve their predictive power and reliability.
