Electronic Structures of Ternary Compounds GeSbTe Based on Machine Learning Empirical Pseudopotentials

Germanium-antimony-telluride (GST) compounds have long been recognized as one of candidate materials for nonvolatile phase-change memory due to high read and write speed and low power consumption. In this study, we present electronic structure calculations of ternary GST compounds using a machine learning empirical pseudopotential method (ML-EPM) [Kim and Son, arXiv:2306.04426 (2023)]. The newly developed ML-EPM method overcomes poor transferability of traditional EPM by ML while retaining its merit such as formal simplicity and less demanding resources. We extend a previous use of ML-EPM from binary to ternary compounds. With a training set of ab initio electronic structures of various GST compounds and their rotation-covariant descriptors, we successfully generate versatile and transferable empirical pseudopotentials of Ge, Sb and Te, respectively. We demonstrate that, using the ML-EPM, computed electronic energy bands and wavefunctions of unlearned GST compounds without cumbersome self-consistency show good agreements with results from first-principles calculations. This agreement holds even for GST crystal structures with distinctive local atomic environments or more extended systems compared to those in training dataset.