AdsMT is a multi-modal Transformer that takes periodic graph representations of catalyst surfaces and feature vectors of adsorbates as inputs to predict the GMAE of each surface/adsorbate combination without any site binding information. As depicted in Fig. , the AdsMT architecture consists of three parts: a graph encoder E G , a vector encoder E V , and a cross-modal encoder E C . In the graph encoder, the unit cell structure of each catalyst surface is modeled as a graph G with periodic invariance by self-connecting edges and radius-based edge construction (see Methods for details). The atom-wise embeddings of surfaces are output from the graph encoder and passed into the cross-modal encoder. Any geometric graph neural network, such as SchNet and GemNet , can serve as the graph encoder in the AdsMT framework.