An integer representation of elements, in terms of atomic number, is straightforward and intuitive for human chemists to learn. However, machine learning models benefit from the descriptive power of a higher dimensional representation, often in the form of continuous element vectors Vi. To effectively represent elements, several types of element embedding have been developed. The Magpie 10 representation, for instance, incorporates diverse element properties such as atomic weights, electronegativity, and melting temperature. embedding comprises chemical descriptor vectors, derived from properties of elements properties. Mat2vec 12 , utilising natural language processing (NLP) techniques, learned material representations from an extensive text corpus, capturing the context and relationships of different materials as mentioned in scientific literature. This method effectively leverages unstructured textual data to enhance understanding of material properties. Skipatom 13 learned representations by predicting the surrounding atomic environment of a target atom based on structural information. It emphasizes capturing the local chemical environments and their impact on material properties. Megnet16 14 utilises graph neural networks, where the embedding is based on graph attributes that include whole graph information. This method employs the weights of the neural networks to predict the formation energy of crystalline materials, treating the atomic structure of materials as a graph with detailed node and edge representations. We employ the Python package ElementEmbeddings 15 to compile the various embeddings.