In terms of data scarcity, AdsMT remains poised for enhancement, as indicated by its unsatisfactory performance on the OCD-GMAE dataset. It was shown that transfer learning is effective in addressing this challenge. In future work, MLIP can be employed to acquire coarse GMAE data for model pretraining, which is much cheaper than LMAE data from DFT calculations. Moreover, it can be particularly interesting to integrate AdsMT with active learning, as it enables the iterative expansion of the training datasets towards underexplored regions of catalyst space and improves the model's reliability.