Despite its potential, data-driven catalysis faces several challenges:
Data Quality: Ensuring the accuracy and consistency of data is crucial. Experimental errors and inconsistencies can lead to misleading insights. Data Integration: Combining data from different sources and formats can be complex and time-consuming. Model Interpretability: Machine learning models, especially deep learning ones, can act as "black boxes," making it difficult to understand the rationale behind their predictions.
