were evaluated on the three GMAE datasets (Fig. and Supplementary To enhance the AdsMT performance under data scarcity, we implemented a transfer learning strategy that entails pre-training on data with local minimum adsorption energy (LMAE). To this end, we established OC20-LMAE, a novel dataset comprising 363,937 surface/adsorbate combinations alongside their LMAEs, derived through data cleaning of the OC20 dataset (Methods). It should be noted that both OCD-GMAE and OC20-LMAE datasets originate from the Open Catalyst Project with analogous surface and adsorbate types, which will be advantageous for transfer learning. As illustrated in Fig. , each AdsMT model undergoes initial pre-training on the OC20-LMAE, followed by fine-tuning on the GMAE datasets while selectively freezing graph encoder parameters. The efficacy of our transfer learning strategy is elucidated in Fig. and Supplementary Table -10, where AdsGT and GNNs reported in the past two years ( ) are chosen as the graph encoders for AdsMT. On the OCD-GMAE dataset, AdsMT models achieve obvious performance gains after transfer learning, resulting in all MAE reductions surpassing 0.14 eV and SR increments exceeding 7 %. Particularly, the ET encoder enables AdsMT to achieve an MAE reduction of 0.291 eV and a 9.3 % increase in SR, and the GemNet-OC encoder facilitates AdsMT to attain an MAE reduction of 0.256 eV and a 9.5 % increase in SR. The best performance of AdsMT on the OCD-GMAE was obtained after transfer learning, yielding an MAE of 0.389 eV and a SR of 22.0 %. On the contrary, transfer learning only provides slight improvements for AdsMT models on the Alloy-GMAE and FG-GMAE, likely attributable to substantial dissimilarities in catalyst surface types between these datasets and OC20-LMAE . Additionally, AdsMT already exhibits commendable predictive performance on these datasets, with MAE values below or proximal to 0.1 eV, indicating limited scope for further performance optimization.