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Empirical tight-binding (ETB) methods have become a common choice to simulate electronic and transport properties for systems composed of thousands of atoms. However, their performance is profoundly dependent on the way the empirical parameters were fitted, and the found parametrizations often exhibit poor transferability. In order to mitigate some of the the criticalities of this method, we introduce a novel Δ-learning scheme, called MLΔTB. After being trained on a custom data set composed of ab-initio band structures, the framework is able to correlate the local atomistic environment to a correction on the on-site ETB parameters, for each atom in the system. The converged algorithm is applied to simulate the electronic properties of random GaAsSb alloys, and displays remarkable agreement both with experimental and ab-initio test data. Some noteworthy characteristics of MLΔTB include the ability to be trained on few instances, to be applied on 3D supercells of arbitrary size, to be rotationally invariant, and to predict physical properties that are not exhibited by the training set.

Abstract Using a density functional theory‐based thermal transport model, which includes the effects of temperature (T)‐dependent potential energy surface, lattice thermal expansion, force constant renormalization, and higher‐order quartic phonon scattering processes, it is found that the recently synthesized nitride perovskite LaWN3 displays strong anharmonic lattice dynamics manifested into a low lattice thermal conductivity (κL) and a non‐standard κL∝T−0.491 dependence. At high T, the departure from the standard κL∝T−1 law originates in the dual particle‐wave behavior of the heat carrying phonons, which includes vibrations tied to the N atoms. While the room temperature κL=2.98 W mK‐1 arises mainly from the conventional particle‐like propagation of phonons, there is also a significant atypical wave‐like phonon tunneling effect, leading to a 20% glass‐like heat transport contribution. The phonon broadening effect lowers the particle‐like contribution but increases the glass‐like one. Upon T increase, the glass‐like contribution increases and dominates above T = 850 K. Overall, the low κL with a weak T‐dependence points to a new utility for LaWN3 in energy technology applications, and motivates synthesis and exploration of nitride perovskites.

A possible solution for the realization of high-efficiency visible light-emitting diodes (LEDs) exploits InGaN-quantum-dot-based active regions. However, the role of local composition fluctuations inside the quantum dots and their effect of the device characteristics have not yet been examined in sufficient detail. Here, we present numerical simulations of a quantum-dot structure restored from an experimental high-resolution transmission electron microscopy image. A single InGaN island with the size of ten nanometers and nonuniform indium content distribution is analyzed. A number of two- and three-dimensional models of the quantum dot are derived from the experimental image by a special numerical algorithm, which enables electromechanical, continuum k→·p→, and empirical tight-binding calculations, including emission spectra prediction. Effectiveness of continuous and atomistic approaches are compared, and the impact of InGaN composition fluctuations on the ground-state electron and hole wave functions and quantum dot emission spectrum is analyzed in detail. Finally, comparison of the predicted spectrum with the experimental one is performed to assess the applicability of various simulation approaches.

Abstract While graphene grain boundaries (GBs) are well characterized experimentally, their influence on transport properties is less understood. As revealed here, phononic thermal transport is vulnerable to GBs even when they are ultra‐narrow and aligned along the temperature gradient direction. Non‐equilibrium molecular dynamics simulations uncover large reductions in the phononic thermal conductivity (κp) along linear GBs comprising periodically repeating pentagon‐heptagon dislocations. Green's function calculations and spectral energy density analysis indicate that the origin of the κp reduction is hidden in the periodic GB strain field, which behaves as a reflective diffraction grating with either diffuse or specular phonon reflections, and represents a source of anharmonic phonon–phonon scattering. The non‐monotonic dependence with dislocation density of κp uncovered here is unaccounted for by the classical Klemens theory. It can help identify GB structures that can best preserve the integrity of the phononic transport.

A crucial goal for increasing thermal energy harvesting will be to progress towards atomistic design strategies for smart nanodevices and nanomaterials. This requires the combination of computationally efficient atomistic methodologies with quantum transport based approaches. Here, we review our recent work on this problem, by presenting selected applications of the PHONON tool to the description of phonon transport in nanostructured materials. The PHONON tool is a module developed as part of the Density-Functional Tight-Binding (DFTB) software platform. We discuss the anisotropic phonon band structure of selected puckered two-dimensional materials, helical and horizontal doping effects in the phonon thermal conductivity of boron nitride-carbon heteronanotubes, phonon filtering in molecular junctions, and a novel computational methodology to investigate time-dependent phonon transport at the atomistic level. These examples illustrate the versatility of our implementation of phonon transport in combination with density functional-based methods to address specific nanoscale functionalities, thus potentially allowing for designing novel thermal devices.