What Challenges Exist in Integrating Machine Learning with Catalysis?
Despite its potential, integrating ML with catalysis is not without challenges:
Data Quality: The effectiveness of ML models depends on the quality of the data they are trained on. Inconsistent or incomplete data can lead to inaccurate predictions. Interpretability: ML models, especially deep learning models, can be seen as "black boxes" that offer little insight into the underlying physical and chemical principles of catalysis. Computational Resources: Training ML models can be computationally intensive, requiring significant resources and specialized hardware. Integration with Existing Systems: Implementing ML solutions into existing catalytic processes can be complex and may require significant changes to workflows and infrastructure.
