Efficient prediction of temperature-dependent elastic and mechanical properties of 2D materials

S M Kastuar; C E Ekuma; Z -L Liu

doi:10.1038/s41598-022-07819-8

Efficient prediction of temperature-dependent elastic and mechanical properties of 2D materials

Sci Rep. 2022 Mar 8;12(1):3776. doi: 10.1038/s41598-022-07819-8.

Authors

S M Kastuar¹, C E Ekuma², Z -L Liu^{3

4}

Affiliations

¹ Department of Physics, Lehigh University, Bethlehem, PA, 18015, USA.
² Department of Physics, Lehigh University, Bethlehem, PA, 18015, USA. che218@lehigh.edu.
³ School of Materials Science and Engineering, Harbin Institute of Technology, Harbin, China.
⁴ College of Physics and Electric Information, Luoyang Normal University, Luoyang, 471934, China.

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.

Abstract

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