What Are the Challenges in Using Machine Learning for Catalysis?
Despite its potential, there are several challenges in using machine learning for catalysis:
Data Quality: The accuracy of ML models heavily depends on the quality and quantity of the data. Incomplete or noisy data can lead to unreliable predictions. Feature Engineering: Identifying the right features that influence catalytic performance is crucial and often requires domain expertise. Model Interpretability: Many ML models, especially deep learning models, are often regarded as "black boxes," making it difficult to interpret their predictions. Computational Resources: Training complex models can be resource-intensive, requiring significant computational power and time.
