What Challenges Exist in Applying Machine Learning to Catalysis?
While ML offers significant advantages, several challenges must be addressed:
Data Quality: The accuracy of ML models depends on the quality and quantity of data available. Incomplete or noisy data can lead to unreliable predictions. Interpretability: ML models, especially deep learning, can be complex and difficult to interpret, making it hard to understand the underlying chemistry. Integration: Combining ML with traditional experimental and theoretical approaches requires effective integration and collaboration across disciplines.
