To evaluate the effectiveness of contrastive learning, element embedding vectors generated by the text encoders, when the text input is an element symbol, are visualized using t-SNE for dimensionality reduction, as illustrated in Figure (b). These embedding vectors are obtained from the class token, the initial token in the BERT architecture. The element embeddings from Crystal CLIP reveal distinct clustering of elements into groups such as transition metals, halogens, and noble gases. In contrast, the baseline BERT model does not display such distinct clustering.