Genetic programming-assisted micromechanical models of graphene origami-enabled metal metamaterials
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Zhao:2022:AM,
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author = "Shaoyu Zhao and Yingyan Zhang and Yihe Zhang and
Wei Zhang2 and Jie Yang and Sritawat Kitipornchai",
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title = "Genetic programming-assisted micromechanical models of
graphene origami-enabled metal metamaterials",
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journal = "Acta Materialia",
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year = "2022",
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volume = "228",
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pages = "117791",
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month = "15 " # apr,
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keywords = "genetic algorithms, genetic programming, Graphene
origami, Mechanical metamaterial, Negative Poisson's
ratio, Machine learning, Functionally graded
metamaterial beam",
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ISSN = "1359-6454",
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URL = "https://www.sciencedirect.com/science/article/pii/S1359645422001781",
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DOI = "doi:10.1016/j.actamat.2022.117791",
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size = "15 pages",
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abstract = "Graphene origami (GOri) enabled metallic metamaterials
are novel nanomaterials simultaneously possessing
negative Poisson's ratio (NPR) and enhanced mechanical
properties that are independent of the
topology/architecture of the structure. Predicting
their material properties via existing micromechanical
models, however, is a great challenge. In this paper, a
highly efficient micromechanical modeling approach
based on molecular dynamics (MD) simulation and genetic
programming (GP) algorithm is developed to address this
key issue. The GP-based Halpin-Tsai model is
extensively trained from MD simulation data to
accurately predict the Young's modulus of GOri/Cu
metamaterials with various GOri folding degrees,
graphene contents and temperatures with a high
coefficient of determination (R2) of ∼0.95.
Meanwhile, the well-trained GP-based rule of mixture
can accurately predict the coefficient of thermal
expansion (CTE), Poisson's ratio and density of
metamaterials with R2 of ∼0.95, ∼0.93 and ∼0.99,
respectively. The excellent agreement between our
estimated results and experimental data shows that the
models developed herein are highly efficient and
accurate in predicting mechanical properties that are
essential for the analysis and design of functionally
graded metal metamaterial composite structures. The
theoretical results demonstrate that the proposed
functionally graded metamaterial beam achieves
significantly improved bending performance.",
-
notes = "also known as \cite{ZHAO2022117791}
School of Civil Engineering, The University of
Queensland, St.Lucia, QLD4072 Australia",
- }
Genetic Programming entries for
Shaoyu Zhao
Yingyan Zhang
Yihe Zhang
Wei Zhang2
Jie Yang
Sritawat Kitipornchai
Citations