2023
DOI: 10.3390/c9030076
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Effective Quantum Graph Models of Some Nonequilateral Graphyne Materials

Abstract: It is shown that it is possible to adapt the quantum graph model of graphene to some types of nonequilateral graphynes considered in the literature; we also discuss the corresponding nanotubes. The proposed models are, in fact, effective models and are obtained through selected boundary conditions and an ad hoc prescription. We analytically recover some results from the literature, in particular, the presence of Dirac cones for α-, β- and (6,6,12)-graphynes; for γ-graphyne, our model presents a band gap (accor… Show more

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“…Optimize generative model parameters θ to minimize either the feature preservation error or the reconstruction error. Various optimization techniques can be applied, including utilizing gradients [76], solving eigenvalue problems [77], or employing rewards [78]. Consider a classical dataset 𝑋 = {𝑥 1 , 𝑥 2 , .…”
Section: Qgms For Data Synthesismentioning
confidence: 99%
“…Optimize generative model parameters θ to minimize either the feature preservation error or the reconstruction error. Various optimization techniques can be applied, including utilizing gradients [76], solving eigenvalue problems [77], or employing rewards [78]. Consider a classical dataset 𝑋 = {𝑥 1 , 𝑥 2 , .…”
Section: Qgms For Data Synthesismentioning
confidence: 99%