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Article
Optimizing isotope substitution in graphene for thermal conductivity minimization by genetic algorithm driven molecular simulations
Applied Physics Letters
  • Michael Davies, Iowa State University
  • Baskar Ganapathysubramanian, Iowa State University
  • Ganesh Balasubramanian, Iowa State University
Document Type
Article
Publication Version
Published Version
Publication Date
3-1-2017
DOI
10.1063/1.4979315
Abstract
We present results from a computational framework integrating genetic algorithm and molecular dynamics simulations to systematically design isotope engineered graphene structures for reduced thermal conductivity. In addition to the effect of mass disorder, our results reveal the importance of atomic distribution on thermal conductivity for the same isotopic concentration. Distinct groups of isotope-substituted graphene sheets are identified based on the atomic composition and distribution. Our results show that in structures with equiatomic compositions, the enhanced scattering by lattice vibrations results in lower thermal conductivities due to the absence of isotopic clusters.
Comments

This article is from Davies, Michael, Baskar Ganapathysubramanian, and Ganesh Balasubramanian. "Optimizing isotope substitution in graphene for thermal conductivity minimization by genetic algorithm driven molecular simulations." Applied Physics Letters 110, no. 13 (2017): 133107. DOI:10.1063/1.4979315. Posted with permission.

Copyright Owner
American Institute of Physics
Language
en
File Format
application/pdf
Citation Information
Michael Davies, Baskar Ganapathysubramanian and Ganesh Balasubramanian. "Optimizing isotope substitution in graphene for thermal conductivity minimization by genetic algorithm driven molecular simulations" Applied Physics Letters Vol. 110 Iss. 13 (2017) p. 133107
Available at: http://works.bepress.com/baskar-ganapathysubramanian/15/