Digital features of chemical elements extracted from local geometries in crystal structures†

Abstract

Computational modelling of materials using machine learning (ML) and historical data has become integral to materials research across physical sciences. The accuracy of predictions for material properties using computational modelling is strongly affected by the choice of the numerical representation that describes a material's composition, crystal structure and constituent chemical elements. Structure, both extended and local, has a controlling effect on properties, but often only the composition of a candidate material is available. However, existing elemental and compositional 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 at an element in a 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 of a material, each atomic site can be quantitatively described by similarity to common local structural motifs; by aggregating these unique 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 prioritisation of elemental substitutions. Based on the latter for predicting crystal structures of binary ionic compounds, LEAFs achieve the state-of-the-art accuracy of 86%. 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.