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Efficient prediction of temperature-dependent elastic and mechanical properties of 2D materials
S M Kastuar1, C E Ekuma2, Z -L Liu3,4
1Department of Physics, Lehigh University, Bethlehem, PA, 18015, USA.
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.
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