What are the Challenges in Implementing AI and ML in Catalysis?
While the potential of AI and ML in catalysis is immense, there are several challenges to their implementation: - Data Quality: The effectiveness of AI and ML models depends on the quality and quantity of data available. Incomplete or noisy data can lead to inaccurate predictions. - Model Interpretability: Some ML models, especially deep learning models, can be difficult to interpret. Understanding how these models arrive at their predictions is crucial for their acceptance in scientific research. - Integration with Existing Systems: Implementing AI and ML requires integrating these technologies with existing experimental and computational workflows, which can be complex and resource-intensive.
