Machine-learning-assisted modeling of alloy ordering phenomena at the electronic scale through electronegativity

Many studies attribute the excellent properties of high-entropy alloys to the ordering-phenomena. It can be known from density functional theory that the macroscopic properties of the system can be described by the electron density. Electronegativity is related to electron density, and models describing ordering can be established based on electronegativity scales through machine learning. In this study, a large dataset was established and predicted the ordered state corresponding to the alloy composition. The accuracy of the model on the test set was 94%. Furthermore, this study used different methods to explain the machine learning model and learned more model information.