arXiv subjects

Jesús Carrete

Publications and source records attributed to Jesús Carrete.

At least 19 recordsLinked to original sources

arXiv2026

Symmetry-based modal analysis of heat transport in molecular dynamics of quasi-1D systems

Detailed analysis of thermal conductivity results obtained from molecular dynamics (MD) trajectories conventionally relies on knowledge of the harmonic vibrational modes of the system. This is the case in methods like Green-Kubo modal analysis (GKMA) and homogeneous nonequilibrium modal analysis (HNEMA). However, arbitrary mixing within degenerate phonon subspaces makes individual modal contributions basis dependent and can obscure their symmetry character. We propose an alternative for quasi-one-dimensional (quasi-1D) systems: we construct the modal basis for the decomposition of the thermal conductivity from line-group projection operators, so that every projected component carries well-defined symmetry labels (including rotational information and parities) and the decomposition is unique at the level of irreducible representations (irreps). Applying the idea to a (10, 0)-(20, 0) WS2-MoS2 double-walled nanotube (DWNT) described by a neuroevolution potential (NEP), we find that at 300 K both HNEMA and GKMA yield statistically consistent total conductivities and allow the identification of several prominent symmetry-adapted conduction channels. The GKMA pair matrix shows that within-block and same-channel cross-k terms account for 75.6% of the total conductivity, while cross-channel correlations contribute 24.4%.

cond-mat.mtrl-sci↗

arXiv2026

Pulgon-tools: A toolkit for analysing and harnessing symmetries in quasi-1D systems

Pulgon-tools is an open-source software package providing building blocks for the analysis and modeling of quasi-one-dimensional (quasi-1D) periodic systems based on line-group theory. While mature libraries exist for space-group detection in three-dimensional crystals, an automated and structure-based identification of line groups has so far been lacking. We present software that integrates four complementary components within a consistent line-group framework: (i) structure generation, (ii) symmetry detection, (iii) irreducible representations (irreps) and character tables and (iv) harmonic interatomic force constants (IFCs) correction. This paper introduces the general code structure and several examples that illustrate some relevant applications of the program.

physics.comp-ph↗

arXiv2025

Structural Optimization in Tensor LEED Using a Parameter Tree and $R$-Factor Gradients

Quantitative low-energy electron diffraction [LEED $I(V)$] is a powerful method for surface-structure determination, based on a direct comparison of experimentally observed $I(V)$ data with computations for a structure model. As the diffraction intensities $I$ are highly sensitive to subtle structural changes, local structure optimization is essential for assessing the validity of a structure model and finding the best-fit structure. The calculation of diffraction intensities is well established, but the large number of evaluations required for reliable structural optimization renders it computationally demanding. The computational effort is mitigated by the tensor-LEED approximation, which accelerates optimization by applying a perturbative treatment of small deviations from a reference structure. Nevertheless, optimization of complex structures is a tedious process. Here, the problem of surface-structure optimization is reformulated using a tree-based data structure, which helps to avoid redundant function evaluations. In the new tensor-LEED implementation presented in this work, intensities are computed on the fly, eliminating limitations of previous algorithms that are limited to precomputed values at a grid of search parameters. It also enables the use of state-of-the-art optimization algorithms. Implemented in \textsc{Python} with the JAX library, the method provides access to gradients of the $R$ factor and supports execution on graphics processing units (GPUs). Based on these developments, the computing time can be reduced by more than an order of magnitude.

cond-mat.mtrl-sci↗

arXiv2025

Accelerating Quantum Monte Carlo Calculations with Set-Equivariant Architectures and Transfer Learning

Machine-learning (ML) ansätze have greatly expanded the accuracy and reach of variational quantum Monte Carlo (QMC) calculations, in particular when exploring the manifold quantum phenomena exhibited by spin systems. However, the scalability of QMC is still compromised by several other bottlenecks, and specifically those related to the actual evaluation of observables based on random deviates that lies at the core of the approach. Here we show how the set-transformer architecture can be used to dramatically accelerate or even bypass that step, especially for time-consuming operators such as powers of the magnetization. We illustrate the procedure with a range of examples structured around quantum spin systems with long-range interactions, and comprising both regressions (to predict observables) and classifications (to detect phase transitions). Moreover, we show how transfer learning can be leveraged to reduce the training cost by reusing knowledge from different systems and smaller system sizes.

quant-ph↗

arXiv2025

Accelerating first-principles molecular-dynamics thermal conductivity calculations for complex systems

Atomistic simulations of heat transport in complex materials are costly and hard to converge. This has led to the development of several noise-reduction techniques applicable to equilibrium molecular-dynamics (MD) simulations. We analyze the performance of those strategies, taking InAs nanowires as our benchmark due to the diverse structures and complex phonon spectra of these quasi-1D systems. We demonstrate how, for low-thermal-conductivity systems, cepstral analysis can reduce computational demands while still delivering accurate results that do not require discarding arbitrary parts of the dataset. However, issues with this approach are revealed when treating high-thermal-conductivity systems, where the thermal conductivity is significantly underestimated. We discuss alternative methods to be used in that situation, relying on uncertainty propagation from independent simulations. We show that the contributions of the covariance matrix have to be included for a quantitative assessment of the error. The combination of these strategies with machine-learning interatomic potentials (MLIPs) provides an accelerated, robust workflow applicable to a diverse set of systems, as our examples using a highly transferable MACE potential illustrate.

cond-mat.mtrl-sci↗

arXiv2025

Ab-initio heat transport in defect-laden quasi-1D systems from a symmetry-adapted perspective

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 or material interfaces. An intriguing question is the role that structural symmetry plays in thermal transport through those defect-laden sections. To address this, we develop a framework that combines representation theory with the mode-resolved Green's function method, enabling a detailed, symmetry-resolved analysis of phonon transmission through defected segments of quasi-1D systems. To avoid artifacts inherent to formalisms developed for bulk 3D systems, we base our analysis on line groups, the appropriate description of the symmetries of quasi-1D structures. This categorization introduces additional quantum numbers that partition the phonon branches into smaller, symmetry-distinct subsets, enabling clearer mode classification. We employ an Allegro-based machine learning potential to obtain the force constants and phonons with near-ab-initio accuracy. We calculate detailed phonon transmission profiles for single- and multi-layer MoS$_\mathrm{2}$-WS$_\mathrm{2}$ nanotubes and connect the transmission probability of each mode to structural symmetry. Surprisingly, we find that pronounced symmetry breaking can suppress scattering by relaxing selection rules and opening additional transmission channels. Molecular dynamics shows that the behavior persists even when anharmonicity is considered. The fact that higher disorder introduced through defects can enhance thermal transport, and not just suppress it, demonstrates the critical role of symmetry in deciphering the nuances of nanoscale thermal transport.

cond-mat.mtrl-sci↗

arXiv2025

msmJAX: Fast and Differentiable Electrostatics on the GPU in Python

We present msmJAX, a Python package implementing the multilevel summation method with B-spline interpolation, a linear-scaling algorithm for efficiently evaluating electrostatic and other long-range interactions in particle-based simulations. Built on the JAX framework, msmJAX integrates naturally with the machine-learning methods that are transforming chemistry and materials science, while also serving as a powerful tool in its own right. It combines high performance with Python's accessibility, offers easy deployment on GPUs, and supports automatic differentiation. We outline the modular design of msmJAX, enabling users to adapt or extend the code, and present benchmarks and examples, including a verification of linear scaling, and demonstrations of its stability in molecular-dynamics simulations.

physics.comp-ph↗

arXiv2025

First- and second-order quantum phase transitions in the long-range unfrustrated antiferromagnetic Ising chain

We study the ground-state phase diagram of an unfrustrated antiferromagnetic Ising chain with longitudinal and transverse fields in the full range of interactions: from all-to-all to nearest-neighbors. First, we solve the model analytically in the strong long-range regime, confirming in the process that a mean-field treatment is exact for this model. We compute the order parameter and the correlations and show that the model exhibits a tricritical point where the phase transition changes from first to second order. This is in contrast with the nearest-neighbor limit where the phase transition is known to be second order. To understand how the order of the phase transition changes from one limit to the other, we tackle the analytically-intractable interaction ranges numerically, using a variational quantum Monte Carlo method with a neural-network-based ansatz, the visual transformer. We show how the first-order phase transition shrinks with decreasing interaction range and establish approximate boundaries in the interaction range for which the first-order phase transition is present. Finally, we establish that the key ingredient to stabilize a first-order phase transition and a tricritical point is the presence of ferromagnetic interactions between spins of the same sublattice on top of antiferromagnetic interactions between spins of different sublattices. Tunable-range unfrustrated antiferromagnetic interactions are just one way to implement such staggered interactions.

quant-ph↗

arXiv2024

Transformer Wave Function for Quantum Long-Range models

We employ a neural-network architecture based on the Vision Transformer (ViT) architecture to find the ground states of quantum long-range models, specifically the transverse-field Ising model for spin-1/2 chains across different interaction regimes. Harnessing the transformer's capacity to capture long-range correlations, we compute the full phase diagram and critical properties of the model, in both the ferromagnetic and antiferromagnetic cases. Our findings show that the ViT maintains high accuracy across the full phase diagram. We compare these results with previous numerical studies in the literature and, in particular, show that the ViT has a superior performance than a restricted-Boltzmann-machine-like ansatz.

quant-ph↗

arXiv2024

Dynamical Disorder in the Mesophase Ferroelectric HdabcoClO4: A Machine-Learned Force Field Study

Hybrid molecular ferroelectrics with orientationally disordered mesophases offer significant promise as lead-free alternatives to traditional inorganic ferroelectrics owing to properties such as room temperature ferroelectricity, low-energy synthesis, malleability, and potential for multiaxial polarization. The ferroelectric molecular salt HdabcoClO4 is of particular interest due to its ultrafast ferroelectric room-temperature switching. However, so far, there is limited understanding of the nature of dynamical disorder arising in these compounds. Here, we employ the neural network NeuralIL to train a machine-learned force field (MLFF) with training data generated using density functional theory. The resulting MLFF-MD simulations exhibit phase transitions and thermal expansion in line with earlier reported experimental results, for both a low-temperature phasetransition coinciding with the orientational disorder of ClO4- molecules and the onset of rotation of Hdabco+ and ClO4- molecules in a high-temperature phase transition. We also find proton transfer even in the low-temperature phase, which increases with temperature and leads to associated proton disorder as well as the onset of disorder in the direction of the hydrogen-bonded chains.

cond-mat.mtrl-sci↗

arXiv2024

Machine-learning potential for phonon transport in AlN with defects in multiple charge states

Understanding phonon transport properties in defect-laden AlN is important for their device applications. Here, we construct a machine-learning potential to describe phonon transport with $ab$ $initio$ accuracy in pristine and defect-laden AlN, following the template of Behler-Parrinello-type neural network potentials (NNPs) but extending them to consider multiple charge states of defects. The high accuracy of our NNP in predicting second- and third-order interatomic force constants is demonstrated through calculations of phonon bands, three-phonon anharmonic, phonon-isotope and phonon-defect scattering rates, and thermal conductivity. In particular, our NNP accurately describes the difference in phonon-related properties among various native defects and among different charge states of the defects. They reveal that the phonon-defect scattering rates induced by V$_{N}^{3+}$ are the largest, followed by V$_{Al}^{3-}$, and that V$_{N}^{1+}$ is the least effective scatterer. This is further confirmed by the magnitude of the respective depressions of the thermal conductivity of AlN. Our findings reveal the significance of the contribution from structural distortions induced by defects to the elastic scattering rates. The present work shows the usefulness of our NNP scheme to cost-efficiently study phonon transport in partially disordered crystalline phases containing charged defects.

cond-mat.mtrl-sci↗

arXiv2024

Neural-network-enabled molecular dynamics study of HfO$_2$ phase transitions

The advances of machine-learned force fields have opened up molecular dynamics (MD) simulations for compounds for which ab-initio MD is too resource-intensive and phenomena for which classical force fields are insufficient. Here we describe a neural-network force field parametrized to reproduce the r2SCAN potential energy landscape of HfO$_2$. Based on an automatic differentiable implementation of the isothermal-isobaric (NPT) ensemble with flexible cell fluctuations, we study the phase space of HfO$_2$. We find excellent predictive capabilities regarding the lattice constants and experimental X-ray diffraction data. The phase transition away from monoclinic is clearly visible at a temperature around 2000 K, in agreement with available experimental data and previous calculations. Another abrupt change in lattice constants occurs around 3000 K. While the resulting lattice constants are closer to cubic, they exhibit a small tetragonal distortion, and there is no associated change in volume. We show that this high-temperature structure is in agreement with the available high-temperature diffraction data.

cond-mat.mtrl-sci↗

arXiv2024

A neural-network-backed effective harmonic potential study of the ambient pressure phases of hafnia

Phonon-based approaches and molecular dynamics are widely established methods for gaining access to a temperature-dependent description of material properties. However, when a compound's phase space is vast, density-functional-theory-backed studies quickly reach prohibitive levels of computational expense. Here, we explore the complex phase structure of HfO2 using effective harmonic potentials based on a neural-network force field (NNFF) as a surrogate model. We detail the data acquisition and training strategy that enable the NNFF to provide almost ab-initio accuracy at a significantly reduced cost and present a recipe for automation. We demonstrate how the NNFF can generalize beyond its training data and that it is transferable between several phases of hafnia. We find that the thermal expansion of the low-symmetry phases agrees well with experimental results and we determine the P-43m phase to be the favorable (stoichiometric) cubic phase over the established Fm-3m. In contrast, the experimental lattice constants of the cubic phases are substantially larger than what is calculated for the corresponding stoichiometric phases. Furthermore, we show that the stoichiometric cubic phases are unlikely to be thermodynamically stable compared to the tetragonal and monoclinic phases, and hypothesize that they only exist in defect-stabilized forms.

cond-mat.mtrl-sci↗

arXiv2024

Electron-induced non-monotonic 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 ($κ_\texttt{ph}$). By using first-principles calculations, we show a non-monotonic pressure dependence of the $κ$ of $θ$-TaN. $κ_\texttt{ph}$ first increases until it reaches a maximum at around 60~GPa, and then decreases. This anomalous behaviour 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 non-monotonic 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 $κ_{\mathrm{ph}}$. At intermediate pressures ($\sim$~20$-$70~GPa), the $κ$ of TaN surpasses that of BAs. Our work provides deeper insight into phonon transport in semimetals and metals where phonon-electron scattering is relevant.

cond-mat.mtrl-sci↗

arXiv2023

Quantitative predictions of the thermal conductivity in transition metal dichalcogenides: The impact of point defects in MoS$_2$ and WS$_2$ monolayers

Transition metal dichalcogenides are investigated for various applications at the nanoscale thanks to 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 MoS$_2$ and WS$_2$ monolayers. We combine Boltzmann transport theory and the 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 experimental data for heat transport and defect concentrations for both MoS$_2$ and WS$_2$. 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 $V_{Mo}\approx V_{2S}^=>V_{2S}^\perp>V_S>S_{ad}$ in both materials, while the scattering rates for the extrinsic (adatom) defects decrease with increasing mass such that Li$_{ad}$>Na$_{ad}$>K$_{ad}$. Compared to 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.

cond-mat.mtrl-sci↗

arXiv2023

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, that can efficiently deal with uncertainties in both the energy and the forces. 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.

physics.comp-ph↗

arXiv2022

Hydrodynamic signatures in thermal transport in devices based on 2D materials: an ab initio study

We investigate the features arising from hydrodynamic effects in graphene and phosphorene devices with finite heat sources, using ab initio calculations to go beyond Callaway's model and inform a full linearized scattering operator, and solving the phonon Boltzmann transport equation through energy-based deviational Monte Carlo methods. We explain the mechanisms that create those hydrodynamic features, showing that boundary scattering and the relation of sample dimensions to the nonlocal length are the determinant factors, regardless of the relative importance of normal versus resistive scattering. From this point of view, the nonlocal length reflects the ability of scattering to randomize the heat flux, and we show that approximations made on the scattering operator may have, through the value of nonlocal length, qualitative consequences on the signatures of hydrodynamic behavior.

cond-mat.mes-hall↗

arXiv2022

Chemical trends in the high thermoelectric performance of the pyrite-type dichalcogenides: ZnS2, CdS2 and CdSe2

The thermoelectric properties of the three pyrite-type IIB-VIA2 dichalcogenides (ZnS2, CdS2 and CdSe2) are systematically investigated and compared with those of the prototype ZnSe2 in order to optimize their thermoelectric properties. Using the phonon Boltzmann transport equation, we find that they all have ultralow lattice thermal conductivities. By analyzing their vibrational properties, these are attributed to soft phonon modes derived from the loosely bound rattling-like metal atoms and to strong anharmonicities caused by the vibrations of all atoms perpendicular to the strongly bound nonmetallic dimers. Additionally, by correlating those properties along the series, we elucidate a number of chemical trends. We find that heavier atom masses, larger atomic displacement parameters and longer bond lengths between metal and nonmetal atoms can be beneficial to the looser rattling of the metal atoms and therefore lead to softer phonon modes, and that stronger nonmetallic dimer bonds can boost the anharmonicities, both leading to lower thermal conductivities. Furthermore, we find that all three compounds have complex energy isosurfaces at valence and conduction band edges that simultaneously allow for large density-of-states effective masses and small conductivity effective masses for both p-type and n-type carriers. Consequently, the calculated thermoelectric figures of merit (ZT), can reach large values both for p-type and n-type doping. Our study illustrates the effects of rattling-like metal atoms and localized nonmetallic dimers on the thermal transport properties and the importance of different carrier effective masses to electrical transport properties in these pyrite-type dichalcogenides, which can be used to predict and optimize the thermoelectric properties of other thermoelectric compounds in the future.

physics.app-ph↗