What are Some Challenges in Applying Machine Learning to Catalysis?
Despite its potential, there are several challenges in applying machine learning to catalysis:
Data Quality: High-quality, consistent data is essential for training accurate models. Inconsistent or incomplete data can lead to unreliable predictions. Complexity: Catalytic systems are often complex, with many interacting variables. Capturing this complexity in a model can be challenging. Interpretability: ML models, especially deep learning models, can be difficult to interpret, making it hard to understand the underlying mechanisms they identify. Generalization: Models trained on specific datasets may not generalize well to different reactions or catalysts.
