Quantum description of transport phenomena: Recent progress

Ji, Wei; Xu, Hongqi; Guo, Hong

doi:10.1007/s11467-014-0458-5

“…For the calculation of current properties, we use the nonequilibrium Green's function (NEGF) method, implemented by the NanoDcal package [39][40][41]. The double ζ-plus polarization numerical orbital basis set is used, where the energy cutoff is taken as 300 Ry and the convergence of the total energy is 10 −5 eV.…”

Section: Model and Methodsmentioning

confidence: 99%

Electronic transport properties of boron and nitrogen pair co-doped 6,6,12-graphyne nanosheet from first principles

Su

¹

,

Qin

²

,

Shao

³

2019

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The electronic transport properties of boron and nitrogen (B–N) pair co-doped 6,6,12-graphyne have been investigated comprehensively by means of the density functional theory combined with the non-equilibrium Green’s function method. In previous studies, the 6,6,12-graphyne represents a small carrier effective mass and high carrier mobility, and its limit in electronic application caused by the closed band gap can be broken through by B–N pair co-doping. It is found that the B–N pair co-doped 6,6,12-graphyne exhibits anisotropic current. The current along the armchair direction is much stronger than that in the zigzag direction. Intriguingly, the current–voltage characteristics generically exhibit a negative differential resistance effect, regardless of the B–N pair doping conformations. In addition, a current rectification effect is observed in the two-probe device models based on the B–N pair co-doped 6,6,12-graphyne. Our results reveal that both the current and rectification effect are intimately connected with the transmission peaks appearing near the Fermi level. These findings suggest that the B–N pair co-doped 6,6,12-graphyne is a promising material for microelectronic device design.

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“…SB model describes a two-state system coupled to infinite number of non-interacting harmonic oscillators. Understanding quantum dissipative dynamics of SB model (or in general open quantum system) has applications in wide range of settings such as quantum computing, 1 quantum memories, 2 quantum electrodynamics, 3,4 organic solar cells, 5 superconducting junctions, 6 quantum biology, 7 quantum optics, 8 quantum transport, 9,10 defect tunneling in solids, 11,12 quantum dots, 13,14 and colour centres and Cooper pair boxes. 15,16 In addition, SB model has remained a key-model for testing and comparison of open quantum system theories before extending them to complex systems.…”

Section: Introductionmentioning

confidence: 99%

Speeding up quantum dissipative dynamics of open systems with kernel methods

Ullah

¹

,

Dral

²

2021

Preprint

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The future forecasting ability of machine learning (ML) makes ML a promising tool for predicting long-time quantum dissipative dynamics of open systems. In this Article, we employ nonparametric machine learning algorithm (kernel ridge regression as a representative of the kernel methods) to study the quantum dissipative dynamics of the widely-used spin-boson model. Our ML model takes short-time dynamics as an input and is used for fast propagation of the long-time dynamics, greatly reducing the computational effort in comparison with the traditional approaches. Presented results show that the ML model performs well in both symmetric and asymmetric spin-boson models. Our approach is not limited to spin-boson model and can be extended to complex systems.

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“…SB model describes a two-state system coupled to infinite number of non-interacting harmonic oscillators. Understanding quantum dissipative dynamics of SB model (or in general open quantum system) has applications in wide range of settings such as quantum computing, 1 quantum memories, 2 quantum electrodynamics, 3,4 organic solar cells, 5 superconducting junctions, 6 quantum biology, 7 quantum optics, 8 quantum transport, 9,10 defect tunneling in solids, 11,12 quantum dots, 13,14 and colour centres and Cooper pair boxes. 15,16 In addition, SB model has remained a key-model for testing and comparison of open quantum system theories before extending them to complex systems.…”

Section: Introductionmentioning

confidence: 99%

Speeding up quantum dissipative dynamics of open systems with kernel methods

Ullah

¹

,

Dral

²

2021

Preprint

View full text Add to dashboard Cite

The future forecasting ability of machine learning (ML) makes ML a promising tool for predicting long-time quantum dissipative dynamics of open systems. In this Article, we employ nonparametric machine learning algorithm (kernel ridge regression as a representative of the kernel methods) to study the quantum dissipative dynamics of the widely-used spin-boson model. Our ML model takes short-time dynamics as an input and is used for fast propagation of the long-time dynamics, greatly reducing the computational effort in comparison with the traditional approaches. Presented results show that the ML model performs well in both symmetric and asymmetric spin-boson models. Our approach is not limited to spin-boson model and can be extended to complex systems.

show abstract

Quantum description of transport phenomena: Recent progress

Cited by 11 publications

References 10 publications

Electronic transport properties of boron and nitrogen pair co-doped 6,6,12-graphyne nanosheet from first principles

Electronic transport properties of boron and nitrogen pair co-doped 6,6,12-graphyne nanosheet from first principles

Speeding up quantum dissipative dynamics of open systems with kernel methods

Speeding up quantum dissipative dynamics of open systems with kernel methods

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