Transfer learning can benefit catalysis in several ways:
Reduced Data Requirements: Catalysis research often involves high-cost and time-consuming experiments. Transfer learning can help by reducing the amount of new data required, leveraging existing datasets to train models. Accelerated Discovery: By using pre-trained models, researchers can quickly identify promising catalytic materials, thereby accelerating the discovery process. Improved Accuracy: Transfer learning can enhance the accuracy of predictive models by incorporating knowledge from related tasks, leading to more reliable predictions in catalysis.
