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

被引：0

作者：

S. M. Kastuar

C. E. Ekuma

Z. -L. Liu

机构：

[1] Lehigh University,Department of Physics

[2] Harbin Institute of Technology,School of Materials Science and Engineering

[3] Luoyang Normal University,College of Physics and Electric Information

来源：

Scientific Reports | / 12卷 / 1期

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摘要：

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.

引用

共 50 条

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