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.
Highlights
The elastic constants can readily be obtained by a polynomial fit of Eq (2)
Stiffness is a measure of the resistance of a material to elastic deformation. We can use it to measure the sound velocity in crystals [62,63] as
The crystal information is provided in the standard crystal information format or the POSCAR format, which is the standard form for VASP code
Summary
For 2D materials, only the in-plane elastic matrix is essential[14,29] The elastic constants can readily be obtained by a polynomial fit of Eq (2). Having obtained the elastic constants, we can compute other related properties. Having obtained the Young’s modulus and the Poisson ratio, we can compute the in-plane stiffness (layer modulus) K, i.e., the 2D equivalent of the bulk modulus and the shear modulus G as[31]
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