Model interpretability is vital for several reasons:
Understanding Mechanisms: Interpretability helps scientists understand the mechanisms of catalytic reactions, such as the role of active sites and the nature of intermediates. Trust and Reliability: Transparent models are more likely to be trusted by researchers and industry professionals. This trust is crucial for the adoption of computational methods in industrial catalysis. Improving Models: Interpretable models allow for the identification of errors and biases, enabling continuous improvement and refinement.
