Transfer learning is a machine learning technique where a model developed for a specific task is reused as the starting point for a model on a second task. This approach is especially valuable when the data for the second task is limited, allowing the model to leverage the knowledge gained from the first task. In the context of catalysis, this can significantly accelerate the discovery and optimization of catalytic materials.
