In this study, we present a Contrastive Learning of Inorganic Crystal Structure (CLICS), as a first step demonstration toward a FM of inorganic materials. We consider two different modalities of crystal structures: graph and text. In the framework of contrastive learning , we have two models each for graph and text embedding, and train these models simultaneously by contrasting their outputs. A graph defines the arrangement of atoms and totally represents the crystal structure as a chemical entity. On the other hand, a text represents contextual patterns of geometric entities that compose the crystal structure, like the local atomic environments of "a Ti atom is bonded to form a TiO 6 octahedral coordination geometry". The intuition behind CLICS is that the structural part of chemical entity can be decomposed into the geometric concepts, and in turn, these concepts integrally represent the chemical entity. Contrasting these two modalities is expected for the models to learn the underlying concepts that connect the chemical and geometric entities (Figure ). A text itself may be considered as a concept; however, what we call concepts are the geometric entities, their classification, and their relationships including the geometric similarities among them. For instance, "tetrahedral" First, named crystal structures (e.g., rock salt), space groups, local atomic environments (e.g., 4-coordinate geometry), and their connections (e.g., corner-sharing octahedra) were separately described (Table shows the count of each concept with an example). We added an unknown class "[UNK]" to the list of space groups as an indefinite label. Crystal systems and points groups were not explicitly considered, but will be partly discussed regarding some hierarchical similarities among space groups. When using the full texts to be contrasted with the graphs, the loss dropped without any performance gain. Thus, we inputted randomly selected N sentences from the texts in each epoch. N was set to 1 or 2 in the experiments, but using more sentences is possible. Each phrasing was randomized in order to avoid fully memorizing the whole sentence, where we would rather let our model to see, e.g., what kind of element is connected how, in just such a short context though. In the validation phase, we inputted for each crystal another phrase that was not used during the training phase (Table ).