What are the challenges in implementing AI-driven systems in catalysis?
Despite the potential benefits, several challenges exist in implementing AI-driven systems in catalysis:
Data Quality: The accuracy of AI predictions depends heavily on the quality and quantity of the input data. Interdisciplinary Expertise: Effective implementation requires collaboration between chemists, data scientists, and engineers. Computational Resources: High-performance computing infrastructure is often necessary to handle large datasets and complex models. Model Interpretability: Understanding how AI models make predictions can be challenging, which may limit their acceptance and application.
