What are the Challenges of Applying ML in Catalysis?
While ML offers significant advantages, its application in catalysis is not without challenges. These include:
Data Quality: The accuracy of ML models depends on the quality and quantity of the data used for training. Incomplete or biased data can lead to unreliable predictions. Interpretability: Many ML models, especially deep learning models, act as "black boxes," making it difficult to understand the underlying mechanisms driving their predictions. Computational Resources: Training complex ML models can be computationally intensive, requiring significant resources.
