Related papers: Machine-learning potential for phonon transport in…

We report that single interatomic potential, developed using Gaussian regression of density functional theory calculation data, has high accuracy and flexibility to describe phonon transport with ab initio accuracy in two different…

Determining defect types and concentrations remains a big challenge of semiconductor materials science. By using ab-initio thermal conductivity calculations we reveal that Ni/vacancy antisites, and not the previously claimed Sn/Zr…

Investigation of charged defects is necessary to understand the properties of semiconductors. While density functional theory calculations can accurately describe the relevant physical quantities, these calculations increase the…

Because of high surface-to-volume ratio, the most prominent size effect limiting thermal transport originates from phonon-surface scattering in nanostructures. Herein we propose the mechanism of phonon scattering by the bond strength…

The impact of point defects on thermal transport in uranium nitride (UN) is investigated using a MLIP combined with Green-Kubo (GK) and normal mode analysis (NMA) methods over 300-1500 K. In pristine UN, temperature-dependent calculations…

We compute both electron- and phonon transmissions in thin disordered silicon nanowires. Our atomistic approach is based on tight-binding and empirical potential descriptions of the electronic and phononic systems, respectively. Surface…

To investigate the transport properties in random alloys, it is important to model the alloy disorder using supercells. Though traditional methods like Virtual Crystal Approximation (VCA) are computationally efficient, the local disorder in…

Tuning thermal transport in semiconductor nanostructures is of great significance for thermal management in information and power electronics. With excellent transport properties, such as ballistic transport, immunity to point defects and…

Due to their aspect ratio and wide range of thermal conductivities, nanotubes hold significant promise as heat-management nanocomponents. Their practical use is, however, often limited by thermal resistance introduced by structural defects…

Using the atomic cluster expansion (ACE) framework, we develop a machine learning interatomic potential for fast and accurately modelling the phonon transport properties of wurtzite aluminum nitride. The predictive power of the ACE…

We develop a computational framework, based on the Boltzmann transport equation, with the ability to compute the thermal transport in nanostructured materials of any geometry using as the only input the bulk thermal conductivity…

While the effect of intrinsic defects on the electronic properties of half-Heusler compounds has been extensively discussed in literature, their effect on the lattice vibrations has received much less attention, due to the prohibitive…

The intrinsic charge transport of stanene is investigated by using density function theory and density function perturbation theory coupled with Boltzmann transport equations from first principles. The accurate Wannier interpolations are…

First-principles based modeling on phonon dynamics and transport using density functional theory and Boltzmann transport equation has proven powerful in predicting thermal conductivity of crystalline materials, but it remains unfeasible for…

Atomic vibrations play a critical role in phonon-assisted electron transitions at defects in solids. However, accurate phonon calculations in defect systems are often hindered by the high computational cost of large-supercell…

The thermal conductance by phonons of a quasi-one-dimensional solid with isotope or defect scattering is studied using the Landauer formalism for thermal transport. The conductance shows a crossover from localized to Ohmic behavior, just as…

The optical properties of defects in solids produce rich physics, from gemstone coloration to single-photon emission for quantum networks. Essential to describing optical transitions is electron-phonon coupling, which can be predicted from…

We combine machine learning (ML)-based neuroevolution potentials (NEP) with anharmonic lattice dynamics and the Boltzmann transport equation (ALD-BTE) to achieve a quantitative and mode-resolved description of thermal transport in…

Two-dimensional fullerene networks have recently attracted increasing interest due to their diverse bonding topologies and mechanically robust architectures. In this work, we develop an accurate machine-learned potential NEP-C$_{24}$ for…

A machine learned interatomic potential for AlN was developed using the ultra-fast force field (UF3) methodology. A strong agreement with density functional theory calculations in predicting key structural and mechanical properties,…