Predictive modeling in catalysis typically involves several steps:
1. Data Collection: Gathering experimental data on various catalysts and their performance metrics. 2. Feature Selection: Identifying key properties (features) that influence catalytic activity, such as electronic structure, surface area, and composition. 3. Model Development: Using statistical methods and machine learning algorithms to create models that relate the features to catalytic performance. 4. Validation: Testing the models against experimental data to evaluate their accuracy. 5. Prediction: Applying the validated models to predict the performance of new or untested catalysts.
