What Are the Challenges in Developing Predictive Models?
Developing predictive models in catalysis is not without its challenges. Some of the key challenges include:
Data Quality: High-quality, consistent datasets are essential for training accurate models. Inconsistent or noisy data can lead to poor model performance. Computational Cost: Many predictive models, especially quantum mechanical models, require significant computational resources, making them expensive and time-consuming. Generalizability: Models trained on specific datasets may not generalize well to new, unseen catalytic systems. Interpretability: Some models, particularly complex machine learning models, can be difficult to interpret, making it challenging to derive actionable insights.
