Despite advancements, several challenges remain in predicting catalytic activity:
Complexity: Catalytic reactions often involve complex mechanisms that are difficult to model accurately. Data quality: Reliable predictions require high-quality experimental data, which can be scarce or inconsistent. Scalability: Computational methods can be resource-intensive, limiting their scalability to large systems. Interpretability: Machine learning models, especially deep learning, can be difficult to interpret, making it hard to understand the underlying factors influencing activity.
