Figure (a) shows the accumulated composition matching ratio as a function of the number of atoms in unit-cell for the models trained with Baseline BERT and Crystal CLIP as text encoders when the input textual description is in general text form. The Crystal CLIP model significantly outperforms the BERT model across a range of atom number, demonstrating a 3 times higher composition matching ratio. This result highlights the effectiveness of the contrastive learning approach in Crystal CLIP, where text embedding vectors are aligned with graph embeddings that capture the spatial configuration of threedimensional structures using equivariant GNNs. However, the composition-matching ratio tends to decline as the number of atoms in the structure increases. It is observed that most mismatches in composition for Crystal CLIP models involve differences in stoichiometric coefficient, rather than in atom types. For instance, when the text input specifies LiO3 with 36 atoms, the model might generate a structure with composition Li8O26 instead of the expected Li9O27. This is due to the nature of the stochastic processes in the generative models. While these models improve the overall distribution under guidance, they cannot always produce the exact conditioning data, particularly when denoising atom types which are categorical variables.