What are the challenges of implementing Machine Learning in Catalysis?
Despite its potential, implementing machine learning in catalysis comes with several challenges. One major issue is the quality of data. Inconsistent or incomplete data can lead to inaccurate predictions. Another challenge is the interpretability of models. Complex ML models, such as deep neural networks, often act as "black boxes," making it difficult to understand how they arrive at their predictions. Additionally, there is a need for interdisciplinary expertise, combining knowledge in chemistry, materials science, and data science.
