Related papers: Machine-learning potential for phonon transport in…
We propose an efficient approach for simultaneous prediction of thermal and electronic transport properties in complex materials. Firstly, a highly efficient machine-learned neuroevolution potential is trained using reference data from…
The monolayer of black phosphorous, or phosphorene, has recently emerged as a new 2D semiconductor with intriguing highly anisotropic transport properties. Existing calculations of its intrinsic phonon-limited electronic transport…
We propose a method to engineer the phonon thermal transport properties of low dimensional systems. The method relies on introducing a predetermined combination of molecular adsorbates, which give rise to antiresonances at frequencies…
The integration of advanced optoelectronic properties in nanoscale devices of group III nitride can be realized by understanding the coupling of charge carriers with optical excitations in these nanostructures. The native defect induced…
We present an approach to calculate ballistic phonon transport that combines the atomistic Green's function (AGF) method with ab initio results. For the inter atomic potential we use the harmonic approach. The equilibrium positions of the…
Phonon surface scattering has been at the core of heat transport engineering in nanoscale structures and devices. Herein, we demonstrate that this phonon pathway can be the sole mechanism only below a characteristic, size-dependent…
Using the atomistic nonequilibrium Green's function, we find that thermal conductance of carbon nanotubes with presence of topological lattice imperfects is remarkably reduced, due to the strong Rayleigh scattering of high-frequency…
Interest in high entropy alloy thermoelectric materials is predicated on achieving ultralow lattice thermal conductivity $\kappa\sub{L}$ through large compositional disorder. However, here we show that for a given mechanism, such as mass…
Charge and thermal conductivities are the most important parameters of carbon nanomaterials as candidates for future electronics. In this paper we address the effects of Anderson type disorder in long semiconductor carbon nanotubes (CNTs)…
Current phonon transport theory based on ground-state calculations has been successful in predicting thermal conductivity at room and medium temperatures but may misrepresent behavior at high temperatures. In this work, we predict the…
We develop a physics-informed neural networks (PINNs) framework for the inverse scattering problem in nuclear physics and apply it to the $P_{3/2}$ partial wave of neutron-alpha elastic scattering. The radial potential is represented by a…
The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces. While their capability for modeling phonon properties is…
The ability to minimize the thermal conductivity of dielectrics with minimal structural intervention that could affect electrical properties is an important capability for engineering thermoelectric efficiency in low-cost materials such as…
The nonlocal electron-phonon couplings in organic semiconductors responsible for the fluctuation of intermolecular transfer integrals has been the center of interest recently. Several irreconcilable scenarios coexist for the description of…
We develop a practical first-principles methodology to determine nonradiative carrier capture coefficients at defects in semiconductors. We consider transitions that occur via multiphonon emission. Parameters in the theory, including…
Combining the efficiency of semi-empirical potentials with the accuracy of quantum mechanical methods, machine-learning interatomic potentials (MLIPs) have significantly advanced atomistic modeling in computational materials science and…
We present a machine learning (ML) method for efficient computation of vibrational thermal expectation values of physical properties from first principles. Our approach is based on the non-perturbative frozen phonon formulation in which…
A deep neural network (DNN) model consisting of two hidden layers was proposed for predicting the immediate environments of specific atoms based on X-ray absorption near-edge spectra (XANES). The output layer of the DNN can be adjusted to…
We use the recently proposed scattering states numerical renormalization group (SNRG) approach to calculate $I(V)$ and the differential conductance through a single molecular level coupled to a local molecular phonon. We also discuss the…
Density functional theory based calculations have been performed for solving the phonon Boltzmann transport equation to investigate the thermal transport properties of monolayer (ML) ZnO under in-plane isotropic biaxial tensile strain. The…