For binary compounds, the distribution patterns of embedding vectors reveal distinct characteristics across different element embeddings. Vectors derived from Mat2vec, Skipatom, and Random element embeddings exhibit a dispersed distribution across the reduced space. In contrast, the embeddings generated using Magpie and Oliynyk show a more concentrated, clustered configuration. Figure captures this phenomenon, presenting the reduced embedding vectors for binary compounds, consistent with those in Figure , but classified into distinct categories according to types of chemical compounds according to the anion present, such as pnictides, halides, chalcogenides, and oxides. Notably, the observed clustering patterns with the Mat2vec, Skipatom, and Random embeddings indicate a pronounced tendency for these vectors to group according to specific types. For instance, the oxide binary compounds (marked as green points) form isolated clusters. Such a tendency suggests that atom types play a significant role in the construction of compositional embeddings, which are derived from a weighted sum of individual element embeddings. On the other hand, the Magpie and Oliynyk embeddings, formulated based on a variety of atomic properties indicate the presence of influential atomistic characteristics that extend beyond merely the types of atom species.