  The traditional display of elements in the periodic table is convenient for
the study of chemistry and physics. However, the atomic number alone is
insufficient for training statistical machine learning models to describe and
extract composition-structure-property relationships. Here, we assess the
similarity and correlations contained within high-dimensional local and
distributed representations of the chemical elements, as implemented in an
open-source Python package ElementEmbeddings. These include element vectors of
up to 200 dimensions derived from known physical properties, crystal structure
analysis, natural language processing, and deep learning models. A range of
distance measures are compared and a clustering of elements into familiar
groups is found using dimensionality reduction techniques. The cosine
similarity is used to assess the utility of these metrics for crystal structure
prediction, showing that they can outperform the traditional radius ratio rules
for the structural classification of AB binary solids.
