The graph encoder employed in the AdsMT model plays a pivotal role in capturing structural and chemical features of catalyst surfaces. Unfortunately, existing GNNs fail to discriminate between top-layer and bottom-layer atoms when representing a surface as a graph. Practically, only the top-layer atoms of surfaces are capable of interacting with adsorbates, rendering them inherently more important than other atoms in terms of adsorption energy. Therefore, we designed a graph transformer called AdsGT specifically for encoding surface graphs. As depicted in Fig. , a novel positional encoding method is proposed to compute the positional feature δ i for each atom based on fractional height relative to the underlying atomic plane. This approach augments the model's understanding of surface structures and differentiates between top and bottom layer atoms. Fig. shows the architecture of the AdsGT encoder, which consists of radial basis function (RBF) expansions, embeddings, and graph attention layers. Different from the conventional graph transformer like Graphormer , the AdsGT layer (Fig. ) adopts an edge-wise attention mechanism, delineated by three sequential steps: edge-wise attention coefficients calculation, edge-wise message calculation, and node update. More details about AdsGT architecture and its positional encoding are described in the Methods. We built three GMAE benchmark datasets named OCD-GMAE, Alloy-GMAE and FG-GMAE from OC20-Dense , Catalysis Hub , and 'functional groups' (FG)-dataset datasets through strict data cleaning (see Methods for details), and each data point represents a unique combination of catalyst surface and adsorbate. As shown in Fig. and Supplementary where the distances between each surface/adsorbate combination are correlated with the differences in feature space . Each surface/adsorbate combination is depicted by the smooth overlap of atomic positions (SOAP) descriptors of surfaces and RDKit descriptors of adsorbates. Fig. demonstrates that three GMAE datasets delineate separate chemical spaces, albeit with certain overlapping regions.