What are the Challenges in Applying Machine Learning to Catalysis?
Despite the potential benefits, several challenges exist:
1. Data Scarcity: High-quality experimental data is often limited, making it difficult to train robust models. 2. Computational Costs: Advanced ML models, especially deep learning, require significant computational resources. 3. Interpreting Models: Understanding and interpreting complex models can be challenging, especially when translating findings into practical catalytic designs. 4. Integration with Experimental Work: Bridging the gap between computational predictions and experimental validation remains a critical hurdle.
