What Are the Challenges in Implementing Machine Learning in Catalysis?
While the potential of ML in catalysis is immense, there are several challenges:
1. Data Quality and Quantity: High-quality, extensive datasets are crucial for training ML models. In catalysis, obtaining such datasets can be difficult and expensive. 2. Interpretability: Many ML models, especially deep learning models, are often seen as "black boxes." Understanding the underlying mechanisms can be challenging. 3. Computational Resources: ML algorithms, particularly deep learning, require significant computational power and resources. 4. Integration with Existing Systems: Integrating ML models with existing experimental workflows and systems can be complex and time-consuming.
