TY - JOUR
T1 - Predicting the mechanical characteristics of hydrogen functionalized graphene sheets using artificial neural network approach
AU - Vijayaraghavan, Venkatesh
AU - Garg, Akhil
AU - Wong, Chee How
AU - Tai, Kang
AU - Bhalerao, Yogesh
N1 - Publisher Copyright:
© 2013, Vijayaraghavan et al.; licensee Springer.
PY - 2013/12
Y1 - 2013/12
N2 - 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.
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.
KW - Artificial neural network
KW - Atomistic simulation
KW - Hydrogen functionalized graphene
KW - Nanomechanics
KW - Tensile
UR - http://www.scopus.com/inward/record.url?scp=85135237068&partnerID=8YFLogxK
U2 - 10.1186/2193-8865-3-83
DO - 10.1186/2193-8865-3-83
M3 - Article
AN - SCOPUS:85135237068
SN - 2008-9244
VL - 3
JO - Journal of Nanostructure in Chemistry
JF - Journal of Nanostructure in Chemistry
IS - 1
M1 - 83
ER -