Predicting the mechanical characteristics of hydrogen functionalized graphene sheets using artificial neural network approach

Venkatesh  Vijayaraghavan; Akhil  Garg; Chee How  Wong; Kang  Tai; Yogesh Jayant Bhalerao

doi:10.1186/2193-8865-3-83

Predicting the mechanical characteristics of hydrogen functionalized graphene sheets using artificial neural network approach

Venkatesh Vijayaraghavan, Akhil Garg, Chee How Wong, Kang Tai, Yogesh Jayant Bhalerao

Research output: Contribution to journal › Article › peer-review

41 Citations (Scopus)

Abstract

The mechanical properties of hydrogen functionalized graphene (HFG) sheets werepredicted in this work by using artificial neural network approach. Thepredictions of tensile strength of HFG sheets made by the proposed approach arecompared to those generated by molecular dynamics simulations. The resultsindicate that our proposed computing technique can be used as a powerful toolfor predicting the tensile strength of the HFG sheet.

Original language	English
Article number	83
Number of pages	5
Journal	Journal of Nanostructure in Chemistry
Volume	3
DOIs	https://doi.org/10.1186/2193-8865-3-83
Publication status	Published - 13 Nov 2013

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10.1186/2193-8865-3-83Licence: CC BY

Cite this

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title = "Predicting the mechanical characteristics of hydrogen functionalized graphene sheets using artificial neural network approach",

abstract = "The mechanical properties of hydrogen functionalized graphene (HFG) sheets werepredicted in this work by using artificial neural network approach. Thepredictions of tensile strength of HFG sheets made by the proposed approach arecompared to those generated by molecular dynamics simulations. The resultsindicate that our proposed computing technique can be used as a powerful toolfor predicting the tensile strength of the HFG sheet.",

author = "Venkatesh Vijayaraghavan and Akhil Garg and Wong, {Chee How} and Kang Tai and Bhalerao, {Yogesh Jayant}",

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AU - Tai, Kang

AU - Bhalerao, Yogesh Jayant

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AB - The mechanical properties of hydrogen functionalized graphene (HFG) sheets werepredicted in this work by using artificial neural network approach. Thepredictions of tensile strength of HFG sheets made by the proposed approach arecompared to those generated by molecular dynamics simulations. The resultsindicate that our proposed computing technique can be used as a powerful toolfor predicting the tensile strength of the HFG sheet.

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