Efficient prediction of temperature-dependent elastic and mechanical properties of 2D materials
Srihari M. Kastuar, Chinedu E. Ekuma, Zhongyue Liu
Abstract
An efficient automated toolkit for predicting the mechanical properties of materials can accelerate new materials design and discovery; this process often involves screening large configurational space in high-throughput calculations. Herein, we present the ElasTool toolkit for these applications. In particular, we use the ElasTool to study diversity of 2D materials and heterostructures including their temperature-dependent mechanical properties, and developed a machine learning algorithm for exploring predicted properties.
Topics & Concepts
Computer scienceThroughputProcess (computing)Machine learningMaterials scienceWirelessTelecommunicationsOperating systemMachine Learning in Materials ScienceGraphene research and applications2D Materials and Applications