VASE: A High-Entropy Alloy Short-Range Order Structural Descriptor for Machine Learning

Abstract

The short-range order (SRO) structure in high-entropy alloys (HEAs) is closely associated with many properties, which can be studied through density functional theory (DFT) calculations. Atomic-scale modeling and calculations require substantial computational resources, and machine learning can provide rapid estimations of DFT results. To describe SRO information in HEAs, a new descriptor based on Voronoi Analysis and Shannon Entropy (VASE) is proposed. Based on Voronoi analysis, the Shannon entropy is introduced to directly characterize atomic spatial arrangement information except for composition and atomic interactions, which is necessary for describing the disorder atomic occupancy in HEAs. The new descriptor is used for predicting the formation energy of FeCoNiAlTiCu system based on machine learning model, which is more accurate than other descriptors (Coulomb matrices, partial radial distribution functions, and Voronoi analysis). Moreover, the model trained based on VASE descriptors exhibits the best predictive performance for unrelaxed structures (24.06 meV/atom). The introduction of Shannon entropy provides an effective representation of atomic arrangement information in HEAs, which is a powerful tool for investigating the SRO phenomena.