Glass transition temperature of chalcogenides predicted by large neural network models

Abstract

Chalcogenide glasses are versatile and complex amorphous inorganic materials with exceptional mechanical, thermal and optoelectronic properties. Among their modern technological applications is the integration in storage class memory devices as both phase change memory (PCM) and ovonic threshold selector components (OTS) [1]. The current research is undergoing the transition from the binary 1PCM-1OTS architecture to more performant and scalable selector-only memory structure.