Despite its potential, data driven research in catalysis faces several challenges:
1. Data Quality: Ensuring the accuracy and consistency of experimental and computational data is crucial for reliable model predictions. 2. Data Integration: Combining data from different sources and formats can be complex, requiring sophisticated data processing and normalization techniques. 3. Model Interpretability: Machine learning models can sometimes act as "black boxes," making it difficult to understand the underlying reasons for their predictions. 4. Scalability: Handling and analyzing large datasets require significant computational resources and efficient algorithms.
