To systematically map the inorganic crystalline chemical space in two dimensions, we utilise three primary dimensionality reduction techniques. These are Principal Component Analysis (PCA) and t-distributed Stochastic Neighbour Embedding (t-SNE) , both implemented using the sklearn library , as well as Uniform Manifold Approximation and Projection (UMAP) , which is implemented using the UMAP Python library.