Efficient Prediction of Temperature-dependent Elastic and Mechanical Properties of 2D Materials
Keyword(s):
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.
2015 ◽
Vol 03
(03)
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pp. 1567-1570
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Keyword(s):
2019 ◽
Vol 7
(3)
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pp. 83-88
2019 ◽
Vol XVI
(4)
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pp. 95-113