  We present a novel approach to generate a fingerprint for crystalline
materials that balances efficiency for machine processing and human
interpretability, allowing its application in both machine learning inference
and understanding of structure-property relationships. Our proposed fingerprint
has two components: one representing the crystal structure and the other
characterizing the chemical composition. To represent the latter we construct a
non-orthogonal space where each axis represents a chemical element and where
the angle between the axes quantifies a measure of the similarity between them.
The chemical composition is then defined by the point on the unit sphere in
this non-orthogonal space. By utilizing dimension reduction techniques we can
construct a two-dimensional global map of the space of the thermodynamically
stable crystalline compounds. Despite their simplicity, such maps succeed in
providing a physical separation of material classes according to basic physical
properties. Moreover, this compositional fingerprint can be used as a versatile
input for machine learning algorithms, supplanting conventional one-hot
representations of the chemical composition.
