arXiv subjects

Jesús Carrete

Publications and source records attributed to Jesús Carrete.

At least 37 records · Page 2Linked to original sources

arXiv2022

BTE-Barna: An extension of almaBTE for thermal simulation of devices based on 2D materials

We present BTE-Barna (Boltzmann Transport Equation - Beyond the Rta for NAnosystems), a software package that extends the Monte Carlo (MC) module of the almaBTE solver of the Peierls-Boltzmann transport equation for phonons (PBTE) to work with nanosystems based on 2D materials with complex geometries. To properly capture how the phonon occupations evolve in momentum space as a result of scattering, we have supplemented the relaxation-time approximation with an implementation of the propagator for the full linearized version of the PBTE. The code can now find solutions for finite and extended devices under the effect of a thermal gradient, with isothermal reservoirs or with an arbitrary initial temperature distribution in space and time, writing out the temperature and heat flux distributions as well as their spectral decompositions. Besides the full deviational MC solver, a number of useful approximations for highly symmetric devices are also included.

physics.comp-ph↗

arXiv2021

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. Based on 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 (<0.05 kcal/mol) in the energies and 70 meV/Å 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.

physics.comp-ph↗

arXiv2021

Accurate effective harmonic potential treatment of the high-temperature cubic phase of Hafnia

HfO$_2$ is an important high-$κ$ dielectric and ferroelectric, exhibiting a complex potential energy landscape with several phases close in energy. It is, however, a strongly anharmonic solid, and thus describing its temperature-dependent behavior is methodologically challenging. We propose an approach based on self-consistent, effective harmonic potentials and higher-order corrections to study the potential energy surface of anharmonic materials. The introduction of a reweighting procedure enables the usage of unregularized regression methods and efficiently harnesses the information contained in every data point obtained from density functional theory. This renders the approach highly efficient and a promising candidate for large-scale studies of materials and phase transitions. We detail the approach and test it on the example of the high-temperature cubic phase of HfO$_2$. Our results for the thermal expansion coefficient, $α_V \approx 3.3\times 10^{-5}$ K$^{-1}$, are in agreement with existing experimental ($α_V \approx 4\pm1\times 10^{-5}$ K$^{-1}$) and theoretical ($α_V \approx 5\pm1\times 10^{-5}$ K$^{-1}$) work. Likewise, the bulk modulus agrees well with experiment. We show the detailed temperature dependence of these quantities.

cond-mat.mtrl-sci↗

arXiv2021

Quantum Self-Consistent Ab-Initio Lattice Dynamics

The Quantum Self-Consistent Ab-Initio Lattice Dynamics package (QSCAILD) is a python library that computes temperature-dependent effective 2nd and 3rd order interatomic force constants in crystals, including anharmonic effects. QSCAILD's approach is based on the quantum statistics of a harmonic model. The program requires the forces acting on displaced atoms of a solid as an input, which can be obtained from an external code based on density functional theory, or any other calculator. This article describes QSCAILD's implementation, clarifies its connections to other methods, and illustrates its use in the case of the SrTiO3 cubic perovskite structure.

cond-mat.mtrl-sci↗

arXiv2020

High-throughput study of the static dielectric constant at high temperatures in oxide and fluoride cubic perovskites

Using finite-temperature phonon calculations and the Lyddane-Sachs-Teller relations, we calculate ab initio the static dielectric constants of 78 semiconducting oxides and fluorides with cubic perovskite structures at 1000 K. We first compare our method with experimental measurements, and we find that it succeeds in describing the temperature dependence and the relative ordering of the static dielectric constant $ε_{DC}$ in the series of oxides BaTiO$_{3}$, SrTiO$_{3}$, KTaO$_{3}$. We show that the effects of anharmonicity on the ion-clamped dielectric constant, on Born charges, and on phonon lifetimes, can be neglected in the framework of our high-throughput study. Based on the high-temperature phonon spectra, we find that the dispersion of $ε_{DC}$ is one order of magnitude larger amongst oxides than fluorides at 1000 K. We display the correlograms of the dielectric constants with simple structural descriptors, and we point out that $ε_{DC}$ is actually well correlated with the infinite-frequency dielectric constant $ε_{\infty}$, even in those materials with phase transitions in which $ε_{DC}$ is strongly temperature-dependent.

cond-mat.mtrl-sci↗

arXiv2020

How do defects limit the ultrahigh thermal conductivity of BAs? A first principles study

The promise enabled by BAs high thermal conductivity in power electronics cannot be assessed without taking into account the reduction incurred when doping the material. Using first principles calculations, we determine the thermal conductivity reduction induced by different group IV impurities in BAs as a function of concentration and charge state. We unveil a general trend, where neutral impurities scatter phonons more strongly than the charged ones. $\text{C}_{\text{B}}$ and $\text{Ge}_{\text{As}}$ impurities show by far the weakest phonon scattering and retain BAs $κ$ values of over $\sim$ 1000 $\text{W}\cdot\text{K}^{-1}\cdot\text{m}^{-1}$ even up to high densities making them ideal n-type and p-type dopants. Furthermore, going beyond the doping compensation threshold associated to Fermi level pinning triggers observable changes in the thermal conductivity. This informs design considerations on the doping of BAs, and it also suggests a direct way to determine the onset of compensation doping in experimental samples.

cond-mat.mtrl-sci↗

arXiv2020

Shortcomings of meta-GGA functionals when describing magnetism

Several recent studies have shown that SCAN, a functional belonging to the meta-generalized gradient approximation (MGGA) family, leads to significantly overestimated magnetic moments in itinerant ferromagnetic metals. However, this behavior is not inherent to the MGGA level of approximation since TPSS, for instance, does not lead to such severe overestimations. In order to provide a broader view of the accuracy of MGGA functionals for magnetism, we extend the assessment to more functionals, but also to antiferromagnetic solids. The results show that to describe magnetism there is overall no real advantage in using a MGGA functional compared to GGAs. For both types of approximation, an improvement in ferromagnetic metals is necessarily accompanied by a deterioration (underestimation) in antiferromagnetic insulators, and vice-versa. We also provide some analysis in order to understand in more detail the relation between the mathematical form of the functionals and the results.

cond-mat.mtrl-sci↗

arXiv2020

Growth, charge and thermal transport of flowered graphene

We report on the structural and transport properties of the smallest dislocation loop in graphene, known as a flower defect. First, by means of advanced experimental imaging techniques, we deduce how flower defects are formed during recrystallization of chemical vapor deposited graphene. We propose that the flower defects arise from a bulge type mechanism in which the flower domains are the grains left over by dynamic recrystallisation. Next, in order to evaluate the use of such defects as possible building blocks for all-graphene electronics, we combine multiscale modeling tools to investigate the structure and the electron and phonon transport properties of large monolayer graphene samples with a random distribution of flower defects. For large enough flower densities, we find that electron transport is strongly suppressed while, surprisingly, hole transport remains almost unaffected. These results suggest possible applications of flowered graphene for electron energy filtering. For the same defect densities, phonon transport is reduced by orders of magnitude as elastic scattering by defects becomes dominant. Heat transport by flexural phonons, key in graphene, is largely suppressed even for very low concentrations.

cond-mat.mtrl-sci↗

arXiv2019

Combined treatment of phonon scattering by electrons and point defects explains the thermal conductivity reduction in highly-doped Si

The mechanisms causing the reduction in lattice thermal conductivity in highly P- and B-doped Si are looked into in detail. Scattering rates of phonons by point defects, as well as by electrons, are calculated from first principles. Lattice thermal conductivities are calculated considering these scattering mechanisms both individually and together. It is found that at low carrier concentrations and temperatures phonon scattering by electrons is dominant and can reproduce the experimental thermal conductivity reduction. However, at higher doping concentrations the scattering rates of phonons by point defects dominate the ones by electrons except for the lowest phonon frequencies. Consequently, phonon scattering by point defects contributes substantially to the thermal conductivity reduction in Si at defect concentrations above $10^{19}$ cm$^{-3}$ even at room temperature. Only when, phonon scattering by both point defects and electrons are taken into account, excellent agreement is obtained with the experimental values at all temperatures.

cond-mat.mtrl-sci↗

arXiv2019

Effect of local chemistry and structure on thermal transport in doped GaAs

Using a first-principles approach, we analyze the impact of \textit{DX} centers formed by S, Se, and Te dopant atoms on the thermal conductivity of GaAs. Our results are in good agreement with experiments and unveil the physics behind the drastically different effect of each kind of defect. We establish a causal chain linking the electronic structure of the dopants to the thermal conductivity of the bulk solid, a macroscopic transport coefficient. Specifically, the presence of lone pairs leads to the formation of structurally asymmetric \textit{DX} centers that cause resonant scattering of incident phonons. The effect of such resonances is magnified when they affect the part of the spectrum most relevant for thermal transport. We show that these resonances are associated with localized vibrational modes in the perturbed phonon spectrum. Finally, we illustrate the connection between flat adjacent minima in the energy landscape and resonant phonon scattering through detailed analyses of the energy landscape of the defective structures.

cond-mat.mtrl-sci↗

arXiv2019

Thermal resistance of GaN/AlN graded interfaces

Compositionally graded interfaces in power electronic devices eliminate dislocations, but they can also decrease thermal conduction, leading to overheating. We quantify the thermal resistances of GaN/AlN graded interfaces of varying thickness using ab initio Green's functions, and compare them with the abrupt interface case. A non-trivial power dependence of the thermal resistance versus interface thickness emerges from the interplay of alloy and mismatch scattering mechanisms. We show that the overall behavior of such graded interfaces is very similar to that of a thin-film of an effective alloy in the length scales relevant to real interfaces.

cond-mat.mtrl-sci↗

arXiv2018

High-Throughput Calculations of Thermal Conductivity in Nanoporous Materials: The Case of Half-Heusler Compounds

Achieving low thermal conductivity and good electrical properties is a crucial condition for thermal energy harvesting materials. Nanostructuring offers a very powerful tool to address both requirements: in nanostructured materials, boundaries preferentially scatter phonons compared to electrons. The search for low-thermal-conductivity nanostructures is typically limited to materials with simple crystal structures, such as silicon, because of the complexity arising from modeling branch- and wave vector- dependent nanoscale heat transport. Using the phonon mean-free-path (MFP) dependent Boltzmann transport equation, a model that overcomes this limitation, we compute thermal transport in 75 nanoporous half-Heusler compounds for different pore sizes. We demonstrate that the optimization of thermal transport in nanostructures should take into account both bulk thermal properties and geometry-dependent phonon suppression, two aspects that are typically engineered separately. In fact, our work predicts that, given a set of bulk materials and a system geometry, the ordering of the thermal conductivity of the nanostructure does not necessarily align with that of the bulk: We show that what dictates thermal transport is the interplay between the bulk MFP distribution and the nanostructuring length scale of the material. Finally, we derive a thermal transport model that enables fast systems screening within large bulk material repositories and a given geometry. Our study motivates the need for a holistic approach to engineering thermal transport and provides a method for high-throughput materials discovery.

cond-mat.mes-hall↗

arXiv2018

Ab initio lattice thermal conductivity of bulk and thin-film $α$-Al$\mathrm{_2}$O$\mathrm{_3}$

The thermal conductivities ($κ$) of bulk and thin-film $α$-Al$_2$O$_3$ are calculated from first principles using both the local density approximation (LDA), and the generalized gradient approximation (GGA) to exchange and correlation. The room temperature single crystal LDA value $\sim39~$W/m$~$K agrees well with the experimental values $\sim35-39~$W/m$~$K, whereas the GGA values are much smaller $\sim$26$~$W/m$~$K. Throughout the temperature range, LDA is found to slightly overestimate $κ$ whereas GGA strongly underestimates it. We calculate the $κ$ of crystalline $α$-Al$\mathrm{_2}$O$\mathrm{_3}$ thin films and observe a maximum of 79$\%$ reduction for $10~$nm thickness.

cond-mat.mtrl-sci↗

arXiv2017

BoltzTraP2, a program for interpolating band structures and calculating semi-classical transport coefficients

BoltzTraP2 is a software package for calculating a smoothed Fourier expression of periodic functions and the Onsager transport coefficients for extended systems using the linearized Boltzmann transport equation. It uses only the band and $k$-dependent quasi-particle energies, as well as the intra-band optical matrix elements and scattering rates, as input. The code can be used via a command-line interface and/or as a Python module. It is tested and illustrated on a simple parabolic band example as well as silicon. The positive Seebeck coefficient of lithium is reproduced in an example of going beyond the constant relaxation time approximation.

cond-mat.mtrl-sci↗

arXiv2017

Phonon transport unveils the prevalent point defects in GaN

Determining the types and concentrations of vacancies present in intentionally doped GaN is a notoriously difficult and long-debated problem. Here we use an unconventional approach, based on thermal transport modeling, to determine the prevalence of vacancies in previous measurements. This allows us to provide conclusive evidence of the recent hypothesis that gallium vacancies in ammonothermally grown samples can be complexed with hydrogen. Our calculations for O-doped and Mg-O co-doped samples yield a consistent picture interlinking dopant and vacancy concentration, carrier density, and thermal conductivity, in excellent agreement with experimental measurements. These results also highlight the predictive power of ab initio phonon transport modeling, and its value for understanding and quantifying defects in semiconductors.

cond-mat.mtrl-sci↗

arXiv2017

First-principles quantitative prediction of the lattice thermal conductivity in random semiconductor alloys: the role of force-constant disorder

The standard theoretical understanding of the lattice thermal conductivity, $κ_{\ell}$, of semiconductor alloys assumes that mass disorder is the most important source of phonon scattering. In contrast, we show that the hitherto neglected contribution of force-constant (IFC) disorder is essential to accurately predict the $κ_{\ell}$ of those polar compounds characterized by a complex atomic-scale structure. We have developed an \emph{ab initio} method based on special quasirandom structures and Green's functions, and including the role of IFC disorder, and applied it in order to calculate the $κ_{\ell}$ of $\mathrm{In_{1-x}Ga_xAs}$ and $\mathrm{Si_{1-x}Ge_x}$ alloys. We show that, while for $\mathrm{Si_{1-x}Ge_x}$, phonon-alloy scattering is dominated by mass disorder, for $\mathrm{In_{1-x}Ga_xAs}$, the inclusion of IFC disorder is fundamental to accurately reproduce the experimentally observed $κ_{\ell}$. As the presence of a complex atomic-scale structure is common to most III-V and II-VI random semiconductor alloys, we expect our method to be suitable for a wide class of materials.

cond-mat.mtrl-sci↗

arXiv2017

The AFLOW Fleet for Materials Discovery

The traditional paradigm for materials discovery has been recently expanded to incorporate substantial data driven research. With the intent to accelerate the development and the deployment of new technologies, the AFLOW Fleet for computational materials design automates high-throughput first principles calculations, and provides tools for data verification and dissemination for a broad community of users. AFLOW incorporates different computational modules to robustly determine thermodynamic stability, electronic band structures, vibrational dispersions, thermo-mechanical properties and more. The AFLOW data repository is publicly accessible online at aflow.org, with more than 1.7 million materials entries and a panoply of queryable computed properties. Tools to programmatically search and process the data, as well as to perform online machine learning predictions, are also available.

cond-mat.mtrl-sci↗

arXiv2017

AFLOW-ML: A RESTful API for machine-learning predictions of materials properties

Machine learning approaches, enabled by the emergence of comprehensive databases of materials properties, are becoming a fruitful direction for materials analysis. As a result, a plethora of models have been constructed and trained on existing data to predict properties of new systems. These powerful methods allow researchers to target studies only at interesting materials $\unicode{x2014}$ neglecting the non-synthesizable systems and those without the desired properties $\unicode{x2014}$ thus reducing the amount of resources spent on expensive computations and/or time-consuming experimental synthesis. However, using these predictive models is not always straightforward. Often, they require a panoply of technical expertise, creating barriers for general users. AFLOW-ML (AFLOW $\underline{\mathrm{M}}$achine $\underline{\mathrm{L}}$earning) overcomes the problem by streamlining the use of the machine learning methods developed within the AFLOW consortium. The framework provides an open RESTful API to directly access the continuously updated algorithms, which can be transparently integrated into any workflow to retrieve predictions of electronic, thermal and mechanical properties. These types of interconnected cloud-based applications are envisioned to be capable of further accelerating the adoption of machine learning methods into materials development.

cond-mat.mtrl-sci↗