Chemistry and Local Environment Adaptive Representation Graphs as Material Descriptors

Accelerating materials design and property prediction via supervised learning involves manually generating feature vectors or performing complicated atom coordinates transformation, which restricts the model to a limited set of crystal structures or makes it challenging to provide chemical insights. In this work, we devised a unique low-dimensional featurization technique known as "Chemistry and Local Environment Adaptive Representation" (CLEAR) graphs to automatically learn the properties of materials through the chemistry of the atom connections. CLEAR is an adaptive featurization method that integrates the chemistry of atoms with their atomic environment using Voronoi nearest neighbours (NN). In addition, integrating the CLEAR descriptors with explainable machine learning models unravels its potential to obtain numerous scientific insights. We applied this approach to study the stability of compositional and configurational diverse high entropy alloys (HEAs). The proposed framework provides a universal low-dimensional representation with chemistry-informed local environment descriptors, which outperforms the prediction of phases and formation energies in HEAs.