What are the Challenges in Implementing ML in Catalysis?
Despite its potential, implementing ML in catalysis comes with challenges, such as:
- Data Quality: Poor quality data can lead to inaccurate models. - Interpretability: Some ML models, like neural networks, are often seen as "black boxes," making it difficult to interpret their predictions. - Computational Resources: Training complex models requires significant computational power. - Integration with Traditional Methods: Bridging the gap between traditional experimental methods and ML predictions can be challenging.
