1.
Sci Rep
; 12(1): 3776, 2022 Mar 08.
Article
in English
| MEDLINE
| ID: mdl-35260681
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.