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MOHAMMAD IMRAN AZIZ
Machine Learning of the Physicothermal Properties
of Graphene
31–39 (2024)
PACS numbers: 63.22.Rc, 65.40.Ba, 65.40.De, 65.80.Ck, 81.05.ue
Graphene is the emerging area of nanomaterials due to their prospect for coming generation
electronic devices. Various studies of graphene have been carried out to investigate the phonon properties,
elastic properties. However, the physicothermal properties such as heat capacity and Gruneisen parameter
have been neglected. The Gruneisen parameter is the main physical parameter in thermal expansion mechanisms.
These parameters are generally positive for some modes, but they are found to be negative for q values in
high-symmetry directions. They are found negative for acoustic mode in case of graphene. The Debye model for
the specific heat at low temperatures is also taken into account for acoustic branches. The heat capacity of
the graphene is also computed. Physicothermal properties investigated by PYTHON program is agreed very close
with the result of other researchers.
KEY WORDS: Hamiltonian mechanics, harmonic oscillators, heat capacity, thermal
properties of graphene
DOI: https://doi.org/10.15407/nnn.22.01.031
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