Despite its advantages, predictive modeling in catalysis faces several challenges:
1. Data Quality and Availability: High-quality, comprehensive datasets are essential for accurate modeling, but they are often scarce. 2. Computational Complexity: Advanced models, especially those involving quantum mechanics, require significant computational resources. 3. Model Interpretability: Complex models, particularly those based on machine learning, can be difficult to interpret and may not always provide clear insights into the underlying chemistry. 4. Scalability: Ensuring that models remain accurate and efficient as they scale to larger systems or more complex reactions.
