Related papers: An Improved Physics Based Numerical Model of Tunne…
Tunneling of an harmonically bound two-body system through an external Gaussian barrier is studied in a schematic model which allows for a better understanding of intricate quantum phenomena. The role of finite size and internal structure…
We investigate the trade-offs between accuracy and efficiency for several flavors of the dissipative mode-space NEGF algorithm with the self-consistent Born approximation for DFT Hamiltonians. Using these models, we then demonstrate the…
To explore whether the density-functional theory non-equilibrium Green's function formalism (DFT-NEGF) provides a rigorous framework for quantum transport, we carried out time-dependent density functional theory (TDDFT) calculations of the…
As a universal quantum mechanical approach to the dynamical many-body problem, the time-dependent density functional theory (TDDFT) might be inadequate to describe crucial observables that rely on two-body evolution behavior, like the…
We present a novel framework for the study of disclinations in two-dimensional active nematic liquid crystals, and topological defects in general. The order tensor formalism is used to calculate exact multi-particle solutions of the…
We derived a fully-closed-form GN-model formula and tested its accuracy of over 7,000 highly randomized Cband system scenarios. By further applying a correction that leverages the large system test-set, we were able to substantially improve…
We provide a characterization of tunneling between coupled topological insulators in 2D and 3D under the influence of a ferromagnetic layer. We explore conditions for such systems to exhibit integer quantum Hall physics and localized…
A physics-based compact model for silicon gate-all-around (GAA) nanowire tunneling FETs (NW-tFETs) with good accuracy has been developed by considering Phonon-Assisted Tunneling (PAT) and transition from Quantum Capacitance Limit (QCL) to…
The creation of tunable open quantum systems is becoming feasible in current experiments with ultracold atoms in low-dimensional traps. In particular, the high degree of experimental control over these systems allows detailed studies of…
Network models are used as efficient representation of materials with complex, interconnected locally one-dimensional structures. They typically accurately capture the mechanical properties of a material, while substantially reducing…
Tensor network algorithms can efficiently simulate complex quantum many-body systems by utilizing knowledge of their structure and entanglement. These methodologies have been adapted recently for solving the Navier-Stokes equations, which…
As semiconductor technologies continue to scale down to the nanoscale, the efficient prediction of material properties becomes increasingly critical. The tight-binding (TB) method is a widely used semi-empirical approach that offers a…
Designing tunneling junctions with abrupt on-off characteristics and high current densities is critical for many different devices including backward diodes and tunneling field effect transistors (TFETs). It is possible to get a sharp, high…
Offloading data traffic from Infrastructure-to-Device (I2D) to Device-to-Device (D2D) communications is a powerful tool for reducing congestion, energy consumption, and spectrum usage of mobile cellular networks. Prior network-level studies…
Scaling field-effect transistors (FETs) into the sub-10-nm regime fundamentally alters the transport mechanism, challenging long-standing design rules. This study investigates monolayer TMD FETs with channel lengths from 12 nm to 3 nm,…
This work is the second in a series focused on ferrofluid bend channel flows. Here, ferrofluid flows in bend channels are modeled using machine learning methods, based on data generated from the CFD simulation discussed in the first work in…
This paper motivates why Real-Time Maude should be well suited to provide a formal semantics and formal analysis capabilities to modeling languages for embedded systems. One can then use the code generation facilities of the tools for the…
Devices built using circuit quantum electrodynamics architectures are one of the most popular approaches currently being pursued to develop quantum information processing hardware. Although significant progress has been made over the…
Predicting the large-scale dynamics of three-dimensional (3D) turbulence is challenging for machine learning approaches. This paper introduces a transformer-based neural operator (TNO) to achieve precise and efficient predictions in the…
Aerodynamic analysis during aircraft design usually involves methods of varying accuracy and spatial resolution, which all have their advantages and disadvantages. It is therefore desirable to create data-driven models which effectively…