What are the Challenges of Integrating AI and ML in Catalysis?
Despite their potential, integrating AI and ML in catalysis presents several challenges:
Data Quality: High-quality, relevant data is essential for training accurate ML models. Inconsistent or incomplete data can lead to unreliable predictions. Model Interpretability: Understanding how ML models make decisions is crucial for gaining scientific insights. Black-box models can be difficult to interpret, limiting their utility in some applications. Computational Resources: Training complex ML models requires significant computational power, which can be a barrier for some research groups. Integration with Experimental Work: Bridging the gap between computational predictions and experimental validation remains a critical challenge. Collaboration between AI experts and experimental chemists is essential.
