The proposed AdsMT model consists of three parts: a graph encoder E G , a vector encoder E V , and a cross-modal encoder E C . Each surface/adsorbate combination c, consisting of a surface graph G c and an adsorbate feature vector p c , is defined as the model input, and the GMAE of the combination is set as the prediction target. Surface graphs and adsorbate feature vectors are passed to the graph encoder E G and the vector encoder E V for embedding learning, respectively. Then, both embeddings are passed to the cross-modal encoder E C for the crossmodal learning and GMAE prediction. The details of these parts are as follows.