arXiv subjects

Thomas D. Kühne

Publications and source records attributed to Thomas D. Kühne.

At least 37 records · Page 2Linked to original sources

arXiv2026

Reducing the Complexity of Density-Matrix Functionals in a Real-Space-Decomposed DF+RDMF Scheme with the Adaptive Cluster Approximation

Reduced density-matrix functional theory (RDMFT) provides a variational route to electronic correlations beyond conventional density-functional approximations, but explicit evaluations of density-matrix functionals still scale exponentially with the number of active one-particle states. We formulate and assess a real-space-decomposed density-functional plus reduced-density-matrix-functional (DF+RDMF) scheme in which the Coulomb interaction is partitioned locally in real space and the RDMF correction is evaluated only for the strongly correlated part of the interaction. The resulting local density-matrix functionals are further compressed using the adaptive cluster approximation (ACA), which performs a unitary rotation of the bath subspace before truncation and therefore preserves the local interaction while reducing the number of explicitly correlated bath states. As a molecular test case, we consider the bending potential of carbon suboxide, C$_3$O$_2$. While semilocal PBE favors a linear molecule, the DF+RDMF/ACA correction stabilizes a bent configuration in qualitative agreement with the quasilinear behavior inferred from spectroscopy. The approach provides a systematic embedding hierarchy for combining density functionals with explicitly correlated density-matrix corrections in extended or spatially inhomogeneous systems.

physics.chem-ph↗

arXiv2026

Predicting the hydrogen bond strength from water reorientation dynamics at short timescales

Path-integral molecular dynamics simulations and electronic structure-based energy decomposition analysis (EDA) are employed to connect hydrogen bond (H-bond) strength, its asymmetry, and the total delocalization energy at the water/air interface to experimentally measurable observables, such as the reorientation dynamics and the sum-frequency generation (SFG) spectrum. Using SFG spectra for distinct layers at the water/air interface, we validate the accuracy of our simulations and report a red-shift from the interface to bulk and a strongly bonded water peak at around 3250 cm$^{-1}$ in the layer closest to bulk. The reorientation dynamics of water molecules slow down from the interface to bulk, which correlates with the SFG results. From our EDA based on absolutely localized molecular orbitals, we observe a strong decline in total delocalization energy from bulk to the interface, as well as a decline in the strength of the strongest donor and acceptor interactions. The asymmetry between the two strongest interactions similarly rises towards the interface, while the importance of interactions from the outer solvation shells is greatly diminished and is lower than previously reported. Finally, we find that the strength of the strongest H-bond donor/acceptor is best correlated with the local minimum of the autocorrelation function resembling the L2 band librational motions. Following that, we propose a simple yet quantitative relationship between H-bond strength and the short-time reorientation dynamics at the water/air interface that could potentially be extended to predict H-bond strength in other hydrophobic systems from experimentally obtainable observables.

physics.chem-ph↗

arXiv2026

Universal Multifractality at the Topological Anderson Insulator Transition

Disorder is ubiquitous in quantum materials, and its interplay with topology can generate phases absent in the clean limit. Using the Haldane model as a minimal setting, we show that disorder not only shifts topological boundaries but also stabilizes a topological Anderson insulator (TAI) between trivial and Chern insulating regimes. Employing the local Chern marker as a real-space topological probe, we map the full phase diagram and demonstrate that the TAI forms a finite domain bounded by trivial and Anderson insulators. Multifractal analysis of low-energy eigenstates at the boundary reveals universal critical spectra, independent of whether disorder generates or destroys topology. These results place topology, localization, and criticality within a unified framework and provide clear benchmarks for real-space diagnostics of disordered topological phases.

cond-mat.mtrl-sci↗

arXiv2026

SCF framework, HF stability and RPA correlation for Jordan-Wigner-transformed spin Hamiltonians on arbitrary coupling topologies

Mapping spins to fermions via the Jordan-Wigner (JW) transformation can render mean-field (Hartree-Fock, HF) descriptions effective for strongly correlated spin systems. As established in recent work, the application of such approaches is not limited by the nonlocal structure of JW strings or by site ordering, because string operators can be absorbed into Thouless rotations of a Slater determinant, and the variational optimization of a unitary Lie-Algebraic similarity transformation removes any ordering dependence. Leveraging these ideas, we develop a self-consistent field (SCF) scheme that expresses the mean-field energy as a functional of the single-particle density matrix, providing an alternative to gradient-based optimization of Thouless parameters. We derive the analytic orbital Hessian to diagnose HF stability and compute ground-state correlation energy through the random-phase approximation (RPA). Benchmark results for the XXZ and J1-J2 model on one- and two-dimensional lattices demonstrate that RPA significantly improves mean-field accuracy.

cond-mat.str-el↗

arXiv2026

The CP-PAW code package for first-principles calculations from a user's perspective

CP-PAW is a combined electronic structure and ab-initio molecular dynamics code to perform mixed quantum and classical simulations of atomistic condensed phase systems, such as solids, liquids, and molecular systems. As the name suggests, the CP-PAW code unifies the all-electron projector augmented-wave method with the Car-Parrinello approach to determine not only the electronic and nuclear ground state of condensed matter, but also to study their properties and dynamics. In addition to briefly outlining the underlying theory, the focus will be on unique aspects of CP-PAW and how to correctly employ them as a user. How to install CP-PAW using the new build system will also be briefly mentioned.

cond-mat.mtrl-sci↗

arXiv2025

Benchmarking Universal Machine Learning Interatomic Potentials on Elemental Systems

The rapid emergence of universal Machine Learning Interatomic Potentials (uMLIPs) has transformed materials modeling. However, a comprehensive understanding of their generalization behavior across configurational space remains an open challenge. In this work, we introduce a benchmarking framework to evaluate both the equilibrium and far-from-equilibrium performance of state-of-the-art uMLIPs, including three MACE-based models, MatterSim, and PET-MAD. Our assessment utilizes Equation-of-State (EOS) tests to evaluate near-equilibrium properties, such as bulk moduli and equilibrium volumes, alongside extensive Minima Hopping (MH) structural searches to probe the global Potential Energy Surface (PES). Here, we assess universality within the fundamental limit of unary (elemental) systems, which serve as a necessary baseline for broader chemical generalization and provide a framework that can be systematically extended to multicomponent materials. We find that while most models exhibit high accuracy in reproducing equilibrium volumes for transition metals, significant performance gaps emerge in alkali and alkaline earth metal groups. Crucially, our MH results reveal a decoupling between search efficiency and structural fidelity, highlighting that smoother learned PESs do not necessarily yield more accurate energetic landscapes.

cond-mat.mtrl-sci↗

arXiv2025

Scalable Machine Learning Model for Energy Decomposition Analysis in Aqueous Systems

Energy decomposition analysis (EDA) based on absolutely localized molecular orbitals provides detailed insights into intermolecular bonding by decomposing the total molecular binding energy into physically meaningful components. Here, we develop a neural network EDA model capable of predicting the electron delocalization energy component of water molecules, which captures the stabilization arising from charge transfer between occupied absolutely localized molecular orbitals of one molecule and the virtual orbitals of another. Exploiting the locality assumption of the electronic structure, our model enables accurate prediction of electron delocalization energies for molecular systems far beyond the size accessible to conventional density functional theory calculations, while maintaining its accuracy. We demonstrate the applicability of our approach by modeling hydration effects in large molecular complexes, specifically in metal-organic frameworks.

physics.chem-ph↗

arXiv2025

In silico investigation of Ba-based ternary chalcogenides for photovoltaic applications

In solar cells, the absorbers are the key components for capturing solar energy and converting photons into electron-hole pairs. The search for high-performance absorbers with advantageous characteristics is an ongoing task for researchers. In this work, we investigated promising and environmentally benign Ba-based ternary chalcogenides for photovoltaic applications. The total number of Ba-based ternary chalcogenides in the Materials Project database was found to be 279. Materials screening based on bandgap size and stability reduced the number of compounds to 19. The performance of an absorber depends on the charge carrier lifetime, which is controlled by non-radiative processes involving defects. Hence, we investigated the intrinsic defects and p-type dopability of the compounds. We identified two Ba-based compounds, namely \ch{BaCu2Se2} and \ch{ZrBaSe3}, as promising absorbers for single-junction and tandem cells and investigated them in detail.

cond-mat.mtrl-sci↗

arXiv2025

Scalable implementations of mean-field and correlation methods based on Lie-algebraic similarity transformation of spin Hamiltonians in the Jordan-Wigner representation

Recent work has highlighted that the strong correlation inherent in spin Hamiltonians can be effectively reduced by mapping spins to fermions via the Jordan-Wigner transformation (JW). The Hartree-Fock method is straightforward in the fermionic domain and may provide a reasonable approximation to the ground state. Correlation with respect to the fermionic mean-field can be recovered based on Lie-algebraic similarity transformation (LAST) with two-body correlators. Specifically, a unitary LAST variant eliminates the dependence on site ordering, while a non-unitary LAST yields size-extensive correlation energies. Whereas the first recent demonstration of such methods was restricted to small spin systems, we present efficient implementations using analytical gradients for the optimization with respect to the mean-field reference and the LAST parameters, thereby enabling the treatment of larger clusters, including systems with local spins s > 1/2.

cond-mat.str-el↗

arXiv2025

The CP2K Program Package Made Simple

CP2K is a versatile open-source software package for simulations across a wide range of atomistic systems, from isolated molecules in the gas phase to low-dimensional functional materials and interfaces, as well as highly symmetric crystalline solids, disordered amorphous glasses, and weakly interacting soft-matter systems in the liquid state and in solution. This review highlights CP2K's capabilities for computing both static and dynamical properties using quantum-mechanical and classical simulation methods. In contrast to the accompanying theory and code paper [J. Chem. Phys. 152, 194103 (2020)], the focus here is on the practical usage and applications of CP2K, with underlying theoretical concepts introduced only as needed.

physics.comp-ph↗

arXiv2025

Revisiting the Abundance of Topological Materials

The classification of topological materials is revisited using advanced computational workflows that integrate hybrid density functional theory calculations with exact Hartree-Fock exchange. Unlike previous studies, our workflow optimizes atomic configurations obtained from the Materials Project Database, followed by precise electronic structure calculations. Our results based on hybrid density functional theory calculations reveal that only 15\% of materials are topologically nontrivial, which is in stark contrast to the previously reported 30\% based on semi-local exchange and correlation functionals. This discrepancy underscores the critical dependence of topological classifications on accurate atomic and electronic structures, rendering the abundance of topological materials much lower than generally assumed.

cond-mat.mtrl-sci↗

arXiv2025

Quantifying Gibbs measures of disordered crystals up to the solid-liquid phase transition

Quantifying the configuration space and the Gibbs measure of thermally disordered condensed matter systems has been a long standing problem. The challenge is to avoid the Gibbs paradox, which forbids any ordering or labeling of the atoms. Our key observation is that the lattice of a thermally disordered condensed matter system, in either solid, liquid or gas phase, can be fully reconstructed from the Voronoi cells of the atoms alone, even if these Voronoi cells are disassembled and randomly scrambled. In the example of the crystalline phase of silicon, the statistics of the Voronoi cells reveals the existence of four, and only four, large facets that are present with probability one for all temperatures up to the solid-liquid melting line. These four largest facets, which separate nearest-neighboring atoms, can be also be used to reconstruct the lattice of the crystal. Hence, their collection supplies the optimal representation of the configuration of the crystal. We conjecture that the existence of Voronoi facets that, despite their large thermal fluctuations, survive with probability one up to the melting temperature, is the fundamental signature of the crystalline solid phase and therefore key to quantifying the Gibbs measure over the entire solid phase.

physics.comp-ph↗

arXiv2025

Benchmarking semi-empirical quantum chemical methods on liquid water

Stimulated by the renewed interest and recent developments in semi-empirical quantum chemical (SQC) methods for noncovalent interactions, we examine the properties of liquid water at ambient conditions by means of molecular dynamics (MD) simulations, both with the conventional NDDO-type (neglect of diatomic differential overlap) methods, e.g. AM1 and PM6, and with DFTB-type (density-functional tight-binding) methods, e.g. DFTB2 and GFN-xTB. Besides the original parameter sets, some specifically reparametrized SQC methods (denoted as AM1-W, PM6-fm, and DFTB2-iBi) targeting various smaller water systems ranging from molecular clusters to bulk are considered as well. The quality of these different SQC methods for describing liquid water properties at ambient conditions are assessed by comparison to well-established experimental data and also to BLYP-D3 density functional theory-based ab initio MD simulations. Our analyses reveal that static and dynamics properties of bulk water are poorly described by all considered SQC methods with the original parameters, regardless of the underlying theoretical models, with most of the methods suffering from too weak hydrogen bonds and hence predicting a far too fluid water with highly distorted hydrogen bond kinetics. On the other hand, the reparametrized force-matchcd PM6-fm method is shown to be able to quantitatively reproduce the static and dynamic features of liquid water, and thus can be used as a computationally efficient alternative to electronic structure-based MD simulations for liquid water that requires extended length and time scales. DFTB2-iBi predicts a slightly overstructured water with reduced fluidity, whereas AM1-W gives an amorphous ice-like structure for water at ambient conditions.

physics.comp-ph↗

arXiv2025

Simultaneous Spin and Point-Group Adaptation in Exact Diagonalization of Spin Clusters

While either spin or point-group adaptation is straightforward when considered independently, the standard technique for factoring isotropic spin Hamiltonians by the total spin S and the irreducible representation of the point-group is limited by the complexity of transformations between different coupling-schemes that are related by site-permutations. To overcome these challenges, we apply projection-operators directly to uncoupled basis-states, enabling the simultaneous treatment of spin and point-group symmetry without the need for recoupling-transformations. This provides a simple and efficient approach for the exact diagonalization of isotropic spin-models that we illustrate with applications to Heisenberg spin-rings and polyhedra, including systems that are computationally inaccessible with conventional coupling-techniques.

cond-mat.str-el↗

arXiv2024

Projective spin adaptation for the exact diagonalization of isotropic spin clusters

Spin Hamiltonians, like the Heisenberg model, are used to describe magnetic properties of exchange-coupled molecules and solids. For finite clusters, physical quantities such as heat capacities, magnetic susceptibilities or neutron-scattering spectra, can be calculated based on energies and eigenstates obtained by exact diagonalization (ED). Utilizing spinrotational symmetry SU(2) to factor the Hamiltonian with respect to total spin S facilitates ED, but the conventional approach to spin-adapting the basis is more intricate than selecting states with a given magnetic quantum number M (the spin z-component), as it relies on irreducible tensor-operator techniques and spin-coupling coefficients. Here, we present a simpler technique based on applying a spin projector to uncoupled basis states. As an alternative to Löwdin's projection operator, we consider a group-theoretical formulation of the projector, which can be evaluated either exactly or approximately using an integration grid. An important aspect is the choice of uncoupled basis states. We present an extension of Löwdin's theorem for s = 1/2 to arbitrary local spin quantum numbers s, which allows for the direct selection of configurations that span a complete, linearly independent basis in an S sector upon spin projection. We illustrate the procedure with a few examples.

cond-mat.str-el↗

arXiv2024

Analytical solutions of symmetric isotropic spin clusters

Spin models like the Heisenberg Hamiltonian effectively describe the interactions of open-shell transition-metal ions on a lattice and can account for various properties of magnetic solids and molecules. Numerical methods are usually required to find exact or approximate eigenstates, but for small clusters with spatial symmetry, analytical solutions exist, and a few Heisenberg systems have been solved in closed form. This paper presents a simple, generally applicable approach to analytically solve isotropic spin clusters, based on adapting the basis to both total-spin and point-group symmetry to factor the Hamiltonian matrix into sufficiently small blocks. We demonstrate applications to small rings and polyhedra, some of which are straightforward to solve by successive spin-coupling for Heisenberg terms only; additional interactions, such as biquadratic exchange or multi-center terms necessitate symmetry adaptation.

cond-mat.str-el↗

arXiv2023

Simulations of Disordered Matter in 3D with the Morphological Autoregressive Protocol (MAP) and Convolutional Neural Networks

Disordered molecular systems such as amorphous catalysts, organic thin films, electrolyte solutions, and water are at the cutting edge of computational exploration today. Traditional simulations of such systems at length-scales relevant to experiments in practice require a compromise between model accuracy and quality of sampling. To remedy the situation, we have developed an approach based on generative machine learning called the Morphological Autoregressive Protocol (MAP) which provides computational access to mesoscale disordered molecular configurations at linear cost at generation for materials in which structural correlations decay sufficiently rapidly. The algorithm is implemented using an augmented PixelCNN deep learning architecture that we previously demonstrated produces excellent results in 2 dimensions (2D) for mono-elemental molecular systems. Here, we extend our implementation to multielemental 3D and demonstrate performance using water as our test system in two scenarios: 1. liquid water, and 2. a sample conditioned on the presence of a rare motif. We trained the model on small-scale samples of liquid water produced using path-integral molecular dynamics simulation including nuclear quantum effects under ambient conditions. MAP-generated water configurations are shown to accurately reproduce the properties of the training set and to produce stable trajectories when used as initial conditions in classical and quantum dynamical simulations. We expect our approach to perform equally well on other disordered molecular systems while offering unique advantages in situations when the disorder is quenched rather than equilibrated.

cond-mat.mes-hall↗

arXiv2023

PECVD and PEALD on polymer substrates (Part II): Understanding and tuning of barrier and membrane properties of thin films

This feature article presents insights concerning the correlation of PECVD and PEALD thin film structures with their barrier or membrane properties. While in principle similar precursor gases and processes can be applied, the adjustment of deposition parameters for different polymer substrates can lead to either an effective diffusion barrier or selective permeabilities. In both cases the understanding of the film growth and the analysis of the pore size distribution and the pore surface chemistry is of utmost importance for the understanding of the related transport properties of small molecules. In this regard the article presents both concepts of thin film engineering and analytical as well as theoretical approaches leading to a comprehensive description of the state of the art in this field. Moreover, based on the presented correlation of film structure and molecular transport properties perspectives of future relevant research in this area is presented.

cond-mat.soft↗