Materials synthesizability and stability prediction using a semi-supervised teacher-student dual neural network

Abstract

A semi-supervised deep neural network (TSDNN) model based on teacher-student architecture is developed for high-performance formation energy and synthesizability prediction by exploiting a large number of unlabelled samples.