What Are the Challenges in Applying Transfer Learning to Catalysis?
While transfer learning offers numerous benefits, it also presents some challenges:
Domain Adaptation: The catalytic systems in the source and target domains may differ significantly, requiring careful adaptation of the pre-trained models. Data Quality: The success of transfer learning heavily depends on the quality and relevance of the data from the source domain. Poor-quality data can lead to inaccurate models. Model Complexity: Catalytic systems can be highly complex, making it challenging to develop models that accurately capture all relevant interactions and dynamics.
