Electronic and thermal signatures of phosphorene grain boundaries under uniaxial strain

in Physical Review Materials (2022), 6(114003),

Two-dimensional materials have great potential for applications as high-performance electronic devices and efficient thermal rectificators. Among them, pristine phosphorene, a single layer of black ... [more ▼]

Two-dimensional materials have great potential for applications as high-performance electronic devices and efficient thermal rectificators. Among them, pristine phosphorene, a single layer of black phosphorus, has shown promising properties such as ultrahigh charge mobility, a tunable band gap, and mechanical flexibility. However, the introduction of extended structural defects such as grain boundaries (GBs) has, in general, a detrimental influence on the electronic and thermal transport properties by causing additional scattering events. In this computational study, based on a combination of a density-functional parametrized tight-binding approach with the Landauer theory of quantum transport, we show that applying a strain can help to partially counteract this effect. We exemplify this by addressing the electronic and phononic transmission of two specific grain boundaries containing 5|7 (GB1) and 4|8 (GB2) defects, respectively. Under uniaxial strain, the electronic band gaps can be reduced for both types of GB, while the respective thermal conductance is only weakly affected despite rather strong changes in the frequency-resolved phonon transmission. The combination of both effects mainly produces an increase of about a factor of 2 in the thermoelectric figure of merit ZT for a GB2 system. Hence, our results provide insights into the manipulation of transport properties as well as the generation of potential thermoelectric materials based on phosphorene. [less ▲]

Nanoscale Phononic Analog of the Ranque-Hilsch Vortex Tube

in Physical Review Applied (2021), 15(034008),

Thermal management is a current global challenge that must be addressed exhaustively. We propose the design of a nanoscale phononic analog of the Ranque-Hilsch vortex tube in which heat flowing at a given ... [more ▼]

Thermal management is a current global challenge that must be addressed exhaustively. We propose the design of a nanoscale phononic analog of the Ranque-Hilsch vortex tube in which heat flowing at a given temperature is split into two different streams going to the two ends of the device, inducing a temperature asymmetry. Our nanoscale prototype consists of two carbon nanotubes (capped and open) connected by molecular chains. The results show that the structural asymmetry in the contact regions is the key factor for producing the flux asymmetry and, hence, the induced temperature-bias effect. The effect can be controlled by tuning the thermal-equilibration temperature, the number of chains, and the chain length. Deposition on a substrate adds another variable to the manipulation of the flux asymmetry but the effect vanishes at very large substrate temperatures. Our study yields insights into the thermal management in nanoscale materials, especially the crucial issue of whether the thermal asymmetry can survive phonon scattering over relatively long distances, and thus provides a starting point for the design of a nanoscale phononic analog of the Ranque-Hilsch vortex tube. [less ▲]

Accurate Many-Body Repulsive Potentials for Density-Functional Tight Binding from Deep Tensor Neural Networks

in Journal of Physical Chemistry Letters (2020), 11(16), 68356843

We combine density-functional tight binding (DFTB) with deep tensor neural networks (DTNN) to maximize the strengths of both approaches in predicting structural, energetic, and vibrational molecular ... [more ▼]

We combine density-functional tight binding (DFTB) with deep tensor neural networks (DTNN) to maximize the strengths of both approaches in predicting structural, energetic, and vibrational molecular properties. The DTNN is used to construct a nonlinear model for the localized many-body interatomic repulsive energy, which so far has been treated in an atom-pairwise manner in DFTB. Substantially improving upon standard DFTB and DTNN, the resulting DFTB-NNrep model yields accurate predictions of atomization and isomerization energies, equilibrium geometries, vibrational frequencies, and dihedral rotation profiles for a large variety of organic molecules compared to the hybrid DFT-PBE0 functional. Our results highlight the potential of combining semiempirical electronic-structure methods with physically motivated machine learning approaches for predicting localized many-body interactions. We conclude by discussing future advancements of the DFTB-NNrep approach that could enable chemically accurate electronic-structure calculations for systems with tens of thousands of atoms. [less ▲]