We trained our model in the framework of contrastive learning between crystal graphs and texts. The crystal graphs were computed from the CIF files of crystals, and then embedded into vectors using a GNN: We employed ALIGNN , which takes into account the bond angles in addition to the lengths. Aggregating the bond angles as well as the lengths is preferable in capturing the geometric concept of crystals. These graphs were contrasted with the texts that were generated using Robocrystallographer and then preprocessed as described in Section 2. These texts were encoded using MatSciBERT , which is a pretrained model of BERT on a corpus of materials science literature. Likely CLIP , we introduced projection heads after the graph embedding and the text embedding to have two copies of a feature space of the same dimension, so that they share the feature space where concepts are learned.