Related papers: Machine-learning potential for phonon transport in…
Understanding the electrical and thermal transport properties of materials is critical to the design of electronics, sensors and energy conversion devices. Computational modeling can accurately predict materials properties but, in order to…
The semiconductor BAs has drawn significant interest due to experimental reports of simultaneous high thermal conductivity and ambipolar charge mobility. The \textit{ab~initio} prediction of high electron and hole mobility assumed the…
The thermoelectric properties of 54 different group 4 half-Heusler (HH) alloys have been studied from first principles. Electronic transport was studied with density functional theory using hybrid functionals facilitated by the $\mathbf{k}…
Predicting atomic-scale crack propagation in aluminum nitride (AlN) is critical for semiconductor reliability but remains prohibitively expensive via molecular dynamics (MD). We develop a diffusion-based generative machine learning model to…
Two-dimensional (2D) semiconductors are promising channel materials for next-generation field-effect transistors (FETs) thanks to their unique mechanical properties and enhanced electrostatic control. However, the performance of these…
Cathode materials undergo various phase transitions during the charge/discharge process, and the structural transitions significantly affect the battery performance. Although phonon properties can provide a direct clue for structural…
Machine learning approaches have recently emerged as powerful tools to probe structure-property relationships in crystals and molecules. Specifically, Machine learning interatomic potentials (MLIP) can accurately reproduce first-principles…
Layered halide perovskites have emerged as potential alternatives to three-dimensional halide perovskites due to their improved stability and larger material phase space, allowing fine-tuning of structural, electronic, and optical…
We propose a physics-guided neural network (PGNN) framework for constructing nucleon-nucleon inverse potentials based on inverse scattering theory. The framework integrates the Phase Function Method (PFM) with a two-stage supervised…
We investigate the transport properties of three terminal carbon based nanojunctions within the scattering matrix approach. The stability of such junctions is subordinated to the presence of nonhexagonal arrangements in the molecular…
For the nanoscale structures, disorder scattering plays a vital role in the carriers' transport, including electrons and high-frequency phonons. The capability for effectively treating the disorders, including both diagonal and off-diagonal…
Local positional disorder in soft, anharmonic materials has emerged as a central factor in shaping their electronic, vibrational, optical, and transport properties. Viewed mainly as a source of performance degradation, recent theoretical…
A number of diverse bulk properties of the zincblende and wurtzite III-V nitrides AlN, GaN, and InN, are predicted from first principles within density functional theory using the plane-wave ultrasoft pseudopotential method, within both the…
Nanoscale thermal transport is governed by the phonon Boltzmann transport equation (BTE). However, simulating the sub-continuum dynamics remains computationally prohibitive due to the high dimensionality of the phase space and the intrinsic…
We consider the physics of transport through quantum dots in the presence of two tunneling paths. The first path sees electrons hopping on and off the dot while the second path is modeled through a potential scattering-like term. To study…
The biphenylene network (BPN) is a notable achievement in recent fabrication endeavors for conceiving new 2D materials. The stability of its boron nitride counterpart, BN-BPN, has been confirmed through numerical investigations. In this…
Defects in semiconductors, traditionally seen as detrimental to electronic device performance, have emerged as potential assets in quantum technologies due to their unique quantum properties. This study investigates the interaction between…
Transport through semiconductor nanostructures is a quantum-coherent process. This paper focuses on systems in which the electron's dynamics is ballistic and the transport is dominated by the scattering from structure boundaries. Opposite…
Thomson scattering (TS) diagnostics provide reliable, minimally perturbative measurements of fundamental plasma parameters, such as electron density ($n_e$) and electron temperature ($T_e$). Deep neural networks can provide accurate…
Here, we present theoretical analysis of electron transport in polyaniline based (PANi) nanofibers assuming the metalic state of the material. To build up this theory we treat conducting polymers as a special kind of granular metals, and we…