  Computational modelling of materials using machine learning, ML, and
historical data has become integral to materials research. The efficiency of
computational modelling is strongly affected by the choice of the numerical
representation for describing the composition, structure and chemical elements.
Structure controls the properties, but often only the composition of a
candidate material is available. Existing elemental descriptors lack direct
access to structural insights such as the coordination geometry of an element.
In this study, we introduce Local Environment-induced Atomic Features, LEAFs,
which incorporate information about the statistically preferred local
coordination geometry for atoms in crystal structure into descriptors for
chemical elements, enabling the modelling of materials solely as compositions
without requiring knowledge of their crystal structure. In the crystal
structure, each atomic site can be described by similarity to common local
structural motifs; by aggregating these features of similarity from the
experimentally verified crystal structures of inorganic materials, LEAFs
formulate a set of descriptors for chemical elements and compositions. The
direct connection of LEAFs to the local coordination geometry enables the
analysis of ML model property predictions, linking compositions to the
underlying structure-property relationships. We demonstrate the versatility of
LEAFs in structure-informed property predictions for compositions, mapping of
chemical space in structural terms, and prioritising elemental substitutions.
Based on the latter for predicting crystal structures of binary ionic
compounds, LEAFs achieve the state-of-the-art accuracy of 86 per cent. These
results suggest that the structurally informed description of chemical elements
and compositions developed in this work can effectively guide synthetic efforts
in discovering new materials.
