arXiv subjects

Sandro Wieser

Publications and source records attributed to Sandro Wieser.

11 recordsLinked to original sources

arXiv2026

Molecular-dynamics-based modal analysis of heat transport in quasi-one-dimensional systems from a symmetry-adapted perspective

Detailed analysis of thermal conductivity results obtained from molecular dynamics (MD) trajectories conventionally relies on knowledge of the harmonic vibrational modes of the system. Such is the case in methods like Green--Kubo modal analysis (GKMA) and homogeneous nonequilibrium modal analysis (HNEMA). This approach faces several shortcomings that become especially important when dealing with nanostructures. Chief among them is scalability, followed by the ambiguity associated to the degeneracy of the large vibrational subspaces defined by a common translational wave number k. 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

Cepstral Analysis to accelerate Green-Kubo thermal conductivity calculations of Metal-Organic Frameworks

Metal-organic frameworks (MOFs) are promising porous materials for applications such as gas storage and separation, where heat transport can critically affect device performance. However, reliable computational prediction of their thermal conductivities remains challenging. In particular, equilibrium molecular-dynamics-based Green-Kubo (GK) simulations, as the most widely used approach, are severely affected by statistical noise. Moreover, they rely on multiple ambiguous, user-defined parameters, which hinder transferability and automation. Here, we demonstrate for metal-organic frameworks that cepstral analysis in combination with GK simulations provides a robust route to massively mitigate these problems, while simultaneously reducing the required sampling times. This is shown for three prototypical frameworks, MOF-5, HKUST-1, and ZIF-8, employing machine-learned moment tensor potentials trained on DFT reference data. In contrast to conventional, direct GK analysis, which shows erratic convergence and strong sensitivity to ad hoc choices of parameters, the cepstral approach yields stable results across a wide range of correlation lengths and achieves convergence within about 1-2 ns of total sampling time. This establishes cepstral analysis base Green-Kubo simulations combined with machine-learned potentials as an efficient, reproducible and automation-ready framework for near ab initio accuracy prediction of thermal transport in MOFs and other complex low-thermal-conductivity materials.

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

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

Analyzing Heat Transport in Crystalline Polymers in Real and Reciprocal Space

Heat transport can be modelled with a variety of approaches in real space (using molecular dynamics) or in reciprocal space (using the Boltzmann transport equation). Employing two conceptually different approaches of each type, we study heat transport in crystalline polyethylene and polythiophene. We find that consistent results can be obtained when using highly efficient and accurate machine-learned potentials, provided that the physical intricacies of the considered materials and methods are correctly accounted for. For polythiophene this turns out to be comparably straightforward, while for polyethylene we find that the inclusion of higher-order anharmonicities is crucial to avoid a massive overestimation of the thermal conductivity. The responsible long-lived phonons are found at relatively high frequencies between 11 THz and 16 THz. This complicates the use of classical statistics in all molecular-dynamics-based approaches.

cond-mat.mtrl-sci↗

arXiv2024

Heat transport in crystalline organic semiconductors: coexistence of phonon propagation and tunneling

Understanding heat transport in organic semiconductors is of fundamental and practical relevance. Therefore, we study the lattice thermal conductivities of a series of (oligo)acenes, where an increasing number of rings per molecule leads to a systematic increase of the crystals' complexity. Temperature-dependent thermal conductivity experiments in these systems disagree with predictions based on the traditional Peierls-Boltzmann framework, which describes heat transport in terms of particle-like phonon propagation. We demonstrate that accounting for additional phonon-tunneling conduction mechanisms through the Wigner Transport Equation resolves this disagreement and quantitatively rationalizes experiments. The pronounced increase of tunneling transport with temperature explains several unusual experimental observations, such as a weak temperature dependence in naphthalene's conductivity and an essentially temperature-invariant conductivity in pentacene. While the anisotropic conductivities within the acene planes are essentially material-independent, the tunneling contributions (and hence the total conductivities) significantly increase with molecular length in the molecular backbone direction, which for pentacene results in a surprising minimum of the thermal conductivity at 300K.

cond-mat.mtrl-sci↗

arXiv2024

Combining Brillouin spectroscopy and machine learned interatomic potentials to probe mechanical properties of metal organic frameworks

The mechanical properties of metal-organic frameworks (MOFs) are of high fundamental and also practical relevance. A particularly intriguing technique for determining anisotropic elastic tensors is Brillouin scattering, which so far has rarely been used for highly complex materials like MOFs. In the present contribution, we apply this technique to study a newly synthesized MOF-type material, referred to as GUT2. We show that when combining the experiments with state-of-the-art simulations of elastic properties and phonon bands (based on machine-learned force fields and dispersion-corrected density-functional theory). This provides a comprehensive understanding of the experimental signals, which are correlated with the longitudinal and transverse sound velocities. Moreover, even when dealing with comparably small single crystals, which limit the range of accessible experimental data, combining the insights from simulations and experiments allows the determination of approximate values for the components of the elastic tensor of the studied material.

cond-mat.mtrl-sci↗

arXiv2023

Machine learned Force-Fields for an ab-initio Quality Description of Metal-Organic Frameworks

Metal-organic frameworks (MOFs) are an incredibly diverse group of highly porous hybrid materials, which are interesting for a wide range of possible applications. For a reliable description of many of their properties accurate computationally highly efficient methods, like force-field potentials (FFPs), are required. With the advent of machine learning approaches, it is now possible to generate such potentials with relatively little human effort. Here, we present a recipe to parametrize two fundamentally different types of exceptionally accurate and computationally highly efficient machine learned potentials, which belong to the moment-tensor and kernel-based potential families. They are parametrized relying on reference configurations generated in the course of molecular dynamics based, active learning runs and their performance is benchmarked for a representative selection of commonly studied MOFs. For both potentials, comparison to a random set of validation structures reveals close to DFT precision in predicted forces and structural parameters of all MOFs. Essentially the same applies to elastic constants and phonon band structures. Additionally, for MOF-5 the thermal conductivity is obtained with full quantitative agreement to single-crystal experiments. All this is possible while maintaining a high degree of computational efficiency, with the obtained machine learned potentials being only moderately slower than the extremely simple UFF4MOF or Dreiding force fields. The exceptional accuracy of the presented FFPs combined with their computational efficiency has the potential of lifting the computational modelling of MOFs to the next level.

cond-mat.mtrl-sci↗

arXiv2020

Identifying the Bottleneck for Heat Transport in Metal-Organic Frameworks

Controlling the transport of thermal energy is key to most applications of metal-organic frameworks. Analyzing the evolution of the effective local temperature, the interfaces between the metal nodes and the organic linkers are identified as the primary bottlenecks for heat conduction. Consequently, changing the bonding strength at that node-linker interface and the mass of the metal atoms can be exploited to tune the thermal conductivity. This insight is generated employing molecular dynamics simulations in conjunction with advanced, ab initio parametrized force fields. The focus of the present study is on MOF-5 as a prototypical example of an isoreticular MOF. Still, the key findings prevail for different node structures and node-linker bonding chemistries. The presented results lay the foundation for developing detailed structure-to-property relationships for thermal transport in MOFs with the goal of devising strategies for the application-specific optimization of heat conduction.

cond-mat.mtrl-sci↗

arXiv2020

Evaluating Computational Shortcuts in Supercell-Based Phonon Calculations of Molecular Crystals: The Instructive Case of Naphthalene

Phonons crucially impact a variety of properties of organic semiconductor materials. For instance, charge- and heat transport depend on low-frequency phonons, while for other properties, such as the free energy, especially high-frequency phonons count. For all these quantities one needs to know the entire phonon band structure, whose simulation becomes exceedingly expensive for more complex systems when using methods like dispersion-corrected density functional theory (DFT). Therefore, in the present contribution we evaluate the performance of more approximate methodologies, including density functional tight binding (DFTB) and a pool of force fields (FF) of varying complexity and sophistication. Beyond merely comparing phonon band structures, we also critically evaluate to what extent derived quantities, like temperature-dependent heat capacities, mean squared thermal displacements and temperature-dependent free energies are impacted by shortcomings in the description of the phonon bands. As a benchmark system, we choose (deuterated) naphthalene, as the only organic semiconductor material for which to date experimental phonon band structures are available in the literature. Overall, the best performance amongst the approximate methodologies is observed for a system-specifically parametrized second-generation force field. Interestingly, in the low-frequency regime also force fields with a rather simplistic model for the bonding interactions (like the General Amber Force Field) perform rather well. As far as the tested DFTB parametrization is concerned, we obtain a significant underestimation of the unit cell volume resulting in a pronounced overestimation of the phonon energies in the low frequency region. This cannot be mended by relying on the DFT-calculated unit cell, since with this unit cell the DFTB phonon frequencies significantly underestimate the experiments.

cond-mat.mtrl-sci↗