Electron-Induced Nonmonotonic Pressure Dependence of the Lattice Thermal Conductivity of θ-TaN.

Recent theoretical and experimental research suggests that θ-TaN is a semimetal with high thermal conductivity (κ), primarily due to the contribution of phonons (κ_{ph}). By using first-principles calculations, we show a nonmonotonic pressure dependence of the κ of θ-TaN. κ_{ph} first increases until it reaches a maximum at around 60 GPa, and then decreases. This anomalous behavior is a consequence of the competing pressure responses of phonon-phonon and phonon-electron interactions, in contrast to the known materials BAs and BP, where the nonmonotonic pressure dependence is caused by the interplay between different phonon-phonon scattering channels. Although TaN has phonon dispersion features similar to BAs at ambient pressure, its response to pressure is different and an overall stiffening of the phonon branches takes place. Consequently, the relevant phonon-phonon scattering weakens as pressure increases. However, the increased electronic density of states near the Fermi level, and specifically the emergence of additional pockets of the Fermi surface at the high-symmetry L point in the Brillouin zone, leads to a substantial increase in phonon-electron scattering at high pressures, driving a decrease in κ_{ph}. At intermediate pressures (∼20-70 GPa), the κ of TaN surpasses that of BAs. Our Letter provides deeper insight into phonon transport in semimetals and metals where phonon-electron scattering is relevant.

Light-driven dynamical tuning of the thermal conductivity in ferroelectrics.

Dynamical tuning of the thermal conductivity in crystals, κ, is critical for thermal management applications, as well as for energy harvesting and the development of novel phononic devices able to perform logic operations with phonons. Such a desired κ control can be achieved in functional materials that experience large structural and phonon variations as a result of field-induced phase transformations. However, this approach is only practical within reduced temperature intervals containing zero-bias phase transition points, since otherwise the necessary driving fields become excessively large and the materials' performances are detrimentally affected. Here, based on first-principles calculations, we propose an alternative strategy for dynamically tuning κ that is operative over broad temperature conditions and realizable in a wide class of materials. By shining light on the archetypal perovskite oxide KNbO3, we predict that ultrafast and reversible ferroelectric-to-paraelectric phase transitions are induced, yielding large and anisotropic κ variations (up to ≈30% at T = 300 K). These light-induced thermal transport shifts can take place at temperatures spanning several hundreds of kelvin and are essentially the result of anharmonic effects affecting the phonon lifetimes.

Quantitative Predictions of the Thermal Conductivity in Transition Metal Dichalcogenides: Impact of Point Defects in MoS2 and WS2 Monolayers.

Transition metal dichalcogenides are investigated for various applications at the nanoscale because of their unique combination of properties and dimensionality. For many of the anticipated applications, heat conduction plays an important role. At the same time, these materials often contain relatively large amounts of point defects. Here, we provide a systematic analysis of the impact of intrinsic and selected extrinsic defects on the lattice thermal conductivity of MoS2 and WS2 monolayers. We combine Boltzmann transport theory and Green's function-based T-matrix approach for the calculation of scattering rates. The force constants for the defect configurations are obtained from density functional theory calculations via a regression approach, which allows us to sample a rather large number of defects at a moderate computational cost and to systematically enforce both the translational and rotational acoustic sum rules. The calculated lattice thermal conductivity is in quantitative agreement with the experimental data for heat transport and defect concentrations for both MoS2 and WS2. Crucially, this demonstrates that the strong deviation from a 1/T temperature dependence of the lattice thermal conductivity observed experimentally can be fully explained by the presence of point defects. We furthermore predict the scattering strengths of the intrinsic defects to decrease in the sequence VMo ≈ V2S= > V2S⊥ > VS > Sad in both materials, while the scattering rates for the extrinsic (adatom) defects decrease with increasing mass such that Liad > Naad > Kad. Compared with earlier work, we find that both intrinsic and extrinsic adatoms are relatively weak scatterers. We attribute this difference to the treatment of the translational and rotational acoustic sum rules, which, if not enforced, can lead to spurious contributions in the zero-frequency limit.

Deep ensembles vs committees for uncertainty estimation in neural-network force fields: Comparison and application to active learning.

A reliable uncertainty estimator is a key ingredient in the successful use of machine-learning force fields for predictive calculations. Important considerations are correlation with error, overhead during training and inference, and efficient workflows to systematically improve the force field. However, in the case of neural-network force fields, simple committees are often the only option considered due to their easy implementation. Here, we present a generalization of the deep-ensemble design based on multiheaded neural networks and a heteroscedastic loss. It can efficiently deal with uncertainties in both energy and forces and take sources of aleatoric uncertainty affecting the training data into account. We compare uncertainty metrics based on deep ensembles, committees, and bootstrap-aggregation ensembles using data for an ionic liquid and a perovskite surface. We demonstrate an adversarial approach to active learning to efficiently and progressively refine the force fields. That active learning workflow is realistically possible thanks to exceptionally fast training enabled by residual learning and a nonlinear learned optimizer.

Neural-network-backed evolutionary search for SrTiO3(110) surface reconstructions.

The determination of atomic structures in surface reconstructions has typically relied on structural models derived from intuition and domain knowledge. Evolutionary algorithms have emerged as powerful tools for such structure searches. However, when density functional theory is used to evaluate the energy the computational cost of a thorough exploration of the potential energy landscape is prohibitive. Here, we drive the exploration of the rich phase diagram of TiO x overlayer structures on SrTiO3(110) by combining the covariance matrix adaptation evolution strategy (CMA-ES) and a neural-network force field (NNFF) as a surrogate energy model. By training solely on SrTiO3(110) 4×1 overlayer structures and performing CMA-ES runs on 3×1, 4×1 and 5×1 overlayers, we verify the transferability of the NNFF. The speedup due to the surrogate model allows taking advantage of the stochastic nature of the CMA-ES to perform exhaustive sets of explorations and identify both known and new low-energy reconstructions.

A Differentiable Neural-Network Force Field for Ionic Liquids.

We present NeuralIL, a model for the potential energy of an ionic liquid that accurately reproduces first-principles results with orders-of-magnitude savings in computational cost. Built on the basis of a multilayer perceptron and spherical Bessel descriptors of the atomic environments, NeuralIL is implemented in such a way as to be fully automatically differentiable. It can thus be trained on ab initio forces instead of just energies, to make the most out of the available data, and can efficiently predict arbitrary derivatives of the potential energy. Using ethylammonium nitrate as the test system, we obtain out-of-sample accuracies better than 2 meV atom-1 (<0.05 kcal mol-1) in the energies and 70 meV Å-1 in the forces. We show that encoding the element-specific density in the spherical Bessel descriptors is key to achieving this. Harnessing the information provided by the forces drastically reduces the amount of atomic configurations required to train a neural network force field based on atom-centered descriptors. We choose the Swish-1 activation function and discuss the role of this choice in keeping the neural network differentiable. Furthermore, the possibility of training on small data sets allows for an ensemble-learning approach to the detection of extrapolation. Finally, we find that a separate treatment of long-range interactions is not required to achieve a high-quality representation of the potential energy surface of these dense ionic systems.

Influence of Onion-like Carbonaceous Particles on the Aggregation Process of Hydrocarbons.

Molecular dynamics simulations are performed to characterize the nucleation behavior of organic compounds in the gas phase. Six basic molecular species are considered-ethylene, propylene, toluene, styrene, ethylbenzene, and para-xylene-in interaction with onion-like carbon nanostructures that model soot nanoparticles (NPs) at room temperature. We identify a shell-to-island aggregation process during the physisorption of aromatic molecules on the soot surface: The molecules tend to first cover the NP in a shell, on top of which additional adsorbates form island-shaped aggregates. We present results for the binding energy, suggesting that the NPs lead to the formation of more stable molecular aggregates in comparison with the pure gas phase. Our findings describe a plausible microscopic mechanism for the active role of soot in the formation and growth of organic particulate matter.

Strain-tunable lattice thermal conductivity of the Janus PtSTe monolayer.

Using first-principles calculations and Boltzmann transport theory, we study the effect of biaxial tensile strain on phonon transport in a Janus PtSTe monolayer. The band gap between the optical and acoustic phonon branches shrinks with increasing strain, resulting in a highly nonlinear monotonic decrease in the lattice thermal conductivity. That reduction reaches close to an order of magnitude when the gap disappears completely under high strains (>8%). This behavior is attributed to a strong enhancement of the anharmonic scattering of acoustic phonons due to the band overlap. Our findings underscore the potential of strain engineering as a class of methods to tune the thermal transport properties of two-dimensional (2D) Janus nanomaterials.

Revisiting the thermal conductivity of Si, Ge and diamond from first principles: roles of atomic mass and interatomic potential.

The thermal conductivity (κ) of nonmetals is determined by the constituent atoms, the crystal structure and interatomic potentials. Although the group-IV elemental solids Si, Ge and diamond have been studied extensively, a detailed understanding of the connection between the fundamental features of their energy landscapes and their thermal transport properties is still lacking. Here, starting from first principles, we analyze those factors, including the atomic mass (m) and the second- (harmonic) and third-order (anharmonic) interatomic force constants (IFCs). Both the second- and third-order IFCs of Si and Ge are very similar, and thus Si and Ge represent ideal systems to understand how the atomic mass alone affectsκ. Although the group velocity (v) decreases with increasing atomic mass (v-1∝m), the phonon lifetime (τ) follows the opposite trend (τ∝m). Since the contribution toκfrom each phonon mode is approximately proportionalv2τ,κis lower for the heavier element, namely Ge. Although the extremely high thermal conductivity of diamond is often attributed to weak anharmonic scattering, the anharmonic component of the interatomic potential is not much weaker than those of Si and Ge, which seems to be overlooked in the literature. In fact, the absolute magnitude of the third-order IFCs is much larger in diamond, and the ratios of the third-order IFCs with respect to the second-order ones are comparable to those of Si and Ge. We also explain the experimentally measuredκof high-quality diamonds (Inyushikinet al2018Phys. Rev. B97144305) by introducing boundary scattering into the picture, and obtain good agreement between calculations and measurements.

Ultrahigh Thermal Conductivity of θ-Phase Tantalum Nitride.

Extracting long-lasting performance from electronic devices and improving their reliability through effective heat management requires good thermal conductors. Taking both three- and four-phonon scattering as well as electron-phonon and isotope scattering into account, we predict that semimetallic θ-phase tantalum nitride (θ-TaN) has an ultrahigh thermal conductivity (κ), of 995 and 820 W m^{-1} K^{-1} at room temperature along the a and c axes, respectively. Phonons are found to be the main heat carriers, and the high κ hinges on a particular combination of factors: weak electron-phonon scattering, low isotopic mass disorder, and a large frequency gap between acoustic and optical phonon modes that, together with acoustic bunching, impedes three-phonon processes. On the other hand, four-phonon scattering is found to be significant. This study provides new insight into heat conduction in semimetallic solids and extends the search for high-κ materials into the realms of semimetals and noncubic crystal structures.

The Effect of Janus Asymmetry on Thermal Transport in SnSSe.

Several ternary "Janus" metal dichalcogenides such as {Mo,Zr,Pt}-SSe have emerged as candidates with significant potential for optoelectronic, piezoelectric, and thermoelectric applications. SnSSe, a natural option to explore as a thermoelectric given that its "parent" structures are SnS2 and SnSe2 has, however, only recently been shown to be mechanically stable. Here, we calculate the lattice thermal conductivities of the Janus SnSSe monolayer along with those of its parent dicalchogenides. The phonon frequencies of SnSSe are intermediate between those of SnSe2 and SnS2; however, its thermal conductivity is the lowest of the three and even lower than that of a random Sn[S0.5Se0.5]2 alloy. This can be attributed to the breakdown of inversion symmetry and manifests as a subtle effect beyond the reach of the relaxation-time approximation. Together with its low favorable power factor, its thermal conductivity confirms SnSSe as a good candidate for thermoelectric applications.

Anisotropic Thermal Conductivity in Few-Layer and Bulk Titanium Trisulphide from First Principles.

We study the thermal conductivity of monolayer, bilayer, and bulk titanium trisulphide (TiS 3 ) by means of an iterative solution of the Boltzmann transport equation based on ab-initio force constants. Our results show that the thermal conductivity of these layers is anisotropic and highlight the importance of enforcing the fundamental symmetries in order to accurately describe the quadratic dispersion of the flexural phonon branch near the center of the Brillouin zone.

Optimisation of the thermoelectric efficiency of zirconium trisulphide monolayers through unixial and biaxial strain.

The goal of this work is to investigate the influence of mechanical deformation on the electronic and thermoelectric properties of ZrS3 monolayers. We employ density functional theory (DFT) calculations at the hybrid HSE06 level to evaluate the response of the electronic band gap and mobilities, as well as the thermopower, the electrical conductivity, the phononic and electronic contributions to the thermal conductivity, and the heat capacity. Direct examination of the electronic band structures reveals that the band gap can be increased by up to 17% under uniaxial strain, reaching a maximum value of 2.32 eV. We also detect large variations in the electrical conductivity, which is multiplied by 3.40 under a 4% compression, but much smaller changes in the Seebeck coefficient. The effects of mechanical deformation on thermal transport are even more significant, with a nearly five-fold reduction of the lattice thermal conductivity under a biaxial strain of -4%. By harnessing a combination of these effects, the thermoelectric figure of merit of strained ZrS3 could be doubled with respect to the unstrained material.

Phonon transport across crystal-phase interfaces and twin boundaries in semiconducting nanowires.

We combine state-of-the-art Green's-function methods and nonequilibrium molecular dynamics calculations to study phonon transport across the unconventional interfaces that make up crystal-phase and twinning superlattices in nanowires. We focus on two of their most paradigmatic building blocks: cubic (diamond/zinc blende) and hexagonal (lonsdaleite/wurtzite) polytypes of the same group-IV or III-V material. Specifically, we consider InP, GaP and Si, and both the twin boundaries between rotated cubic segments and the crystal-phase boundaries between different phases. We reveal the atomic-scale mechanisms that give rise to phonon scattering in these interfaces, quantify their thermal boundary resistance and illustrate the failure of common phenomenological models in predicting those features. In particular, we show that twin boundaries have a small but finite interface thermal resistance that can only be understood in terms of a fully atomistic picture.

Coupling of Spinons with Defects and Phonons in the Spin Chain Compound Ca_{2}CuO_{3}.

Extrinsic spinon scattering by defects and phonons instead of intrinsic spinon-spinon coupling is responsible for resistive magnetic heat transport in one-dimensional (1D) quantum magnets. Here we report an investigation of the elusive extrinsic effect in the 1D Heisenberg S=1/2 spin chain compound Ca_{2}CuO_{3}, where the defect concentration is determined from the measured specific heat and first-principles calculations are used to separate the lattice component of the measured thermal conductivity to isolate a large magnetic contribution (κ_{m}). The obtained temperature-dependent spinon-defect and spinon-phonon mean free paths can enable a quantitative understanding of both κ_{m} and the spinon-induced spin Seebeck effect.

Using nanotubes to study the phonon spectrum of two-dimensional materials.

We propose a convenient method to characterize the acoustic phonon branches of 2D monolayer materials using measurements of the infrared- and Raman-active vibrational modes of nanotubes. The relations we employ are derived from a symmetry analysis based directly on the line groups of nanotubes. We perform extensive ab initio calculations for the MoS2 monolayer and nanotubes to evaluate the method and illustrate all our results. Specifically, we show how the low-energy phonon transmission, a determining factor in thermal transport, can be easily and successfully reconstructed by this procedure.

Phonon Scattering by Dislocations in GaN.

Crystal imperfections such as dislocations strongly influence the performance and thermal transport behavior of GaN-based devices. We show that the experimental data used to parametrize the effect of dislocations on the thermal conductivity can be explained using only the reported film thickness and point defect concentrations. The analysis highlights the boundary-scattering-governed reduction of thermal conductivity in GaN, which had been underestimated in earlier models. To quantify the influence of dislocations on the thermal transport in GaN, we adopt a Green's function approach based on accurate ab initio interatomic force constants. While calculations at the level of density functional theory are necessary for three-phonon and point defect scattering, we show that scattering due to dislocations can be satisfactorily approximated using semiempirical potentials. This makes the Green's function approach to dislocation scattering a quantitatively predictive, yet computationally practical, method for obtaining detailed phonon scattering rates.

Vibrational Properties of Metastable Polymorph Structures by Machine Learning.

Despite vibrational properties being critical for the ab initio prediction of finite-temperature stability as well as thermal conductivity and other transport properties of solids, their inclusion in ab initio materials repositories has been hindered by expensive computational requirements. Here we tackle the challenge, by showing that a good estimation of force constants and vibrational properties can be quickly achieved from the knowledge of atomic equilibrium positions using machine learning. A random-forest algorithm trained on 121 different mechanically stable structures of KZnF3 reaches a mean absolute error of 0.17 eV/Å2 for the interatomic force constants, and it is less expensive than training the complete force field for such compounds. The predicted force constants are then used to estimate phonon spectral features, heat capacities, vibrational entropies, and vibrational free energies, which compare well with the ab initio ones. The approach can be used for the rapid estimation of stability at finite temperatures.

Materials Screening for the Discovery of New Half-Heuslers: Machine Learning versus ab Initio Methods.

Machine learning (ML) is increasingly becoming a helpful tool in the search for novel functional compounds. Here we use classification via random forests to predict the stability of half-Heusler (HH) compounds, using only experimentally reported compounds as a training set. Cross-validation yields an excellent agreement between the fraction of compounds classified as stable and the actual fraction of truly stable compounds in the ICSD. The ML model is then employed to screen 71 178 different 1:1:1 compositions, yielding 481 likely stable candidates. The predicted stability of HH compounds from three previous high-throughput ab initio studies is critically analyzed from the perspective of the alternative ML approach. The incomplete consistency among the three separate ab initio studies and between them and the ML predictions suggests that additional factors beyond those considered by ab initio phase stability calculations might be determinant to the stability of the compounds. Such factors can include configurational entropies and quasiharmonic contributions.

Exceptionally Strong Phonon Scattering by B Substitution in Cubic SiC.

We use ab initio calculations to predict the thermal conductivity of cubic SiC with different types of defects. An excellent quantitative agreement with previous experimental measurements is found. The results unveil that B_{C} substitution has a much stronger effect than any of the other defect types in 3C-SiC, including vacancies. This finding contradicts the prediction of the classical mass-difference model of impurity scattering, according to which the effects of B_{C} and N_{C} would be similar and much smaller than that of the C vacancy. The strikingly different behavior of the B_{C} defect arises from a unique pattern of resonant phonon scattering caused by the broken structural symmetry around the B impurity.