arXiv subjects

Lukas Hörmann

Publications and source records attributed to Lukas Hörmann.

16 recordsLinked to original sources

arXiv2026

Comprehensive Structure Exploration and Thermodynamics of Graphitic Heteroatom Doped Graphene Superstructures

Graphene has been studied in detail due to its mechanical, electrical, and thermal properties. It is well documented that the introduction of dopants or defects in the lattice can be used to tune material properties for a specific application, such as in electronics, sensors, or catalysis. To design graphene with specific properties, one must achieve control over the composition and concentration of defects. This requires a fundamental understanding of the stability of defects and their interaction in a superstructure. We present a comprehensive defect structure determination approach that enables close to exhaustive enumeration of all relevant defect structures. The approach uses a combination of Density Functional Theory and machine learning to build a transferable energy model for defect formation. We show the capabilities of our approach for free-standing graphene with heteroatom defects, establishing a thermodynamic model to investigate how temperature affects the configuration space of doped graphene. Our analysis yields physical insights into defect interactions. A characteristic peak in the heat capacity indicates a structural transition. We provide a mechanistic explanation for this behavior and demonstrate how the transition temperature shifts with different thermodynamic reservoirs.

cond-mat.mtrl-sci↗

arXiv2026

Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles

Foundation machine learning interatomic potentials (MLIPs) are increasingly being used as drop-in replacements for first-principles calculations, enabling simulations of materials at length and time scales that were previously inaccessible. However, due to lack of ground truth data, their accuracy on structural and dynamical observables in finite thermodynamic ensembles is yet to be established. Here, we introduce Dyna-Mat-v1.0, a benchmark dataset of condensed-phase first-principles molecular dynamics trajectories designed to test foundation MLIPs at realistic finite-temperature conditions. Using this dataset, we evaluate 15 foundation MLIPs across four model tiers by comparing both single-point energy and force errors on first-principles configurations and observables generated from MLIP-driven trajectories. We find that "on average" models with lower single-point force errors also yield lower errors for structural and dynamical observables. However, there are individual systems for which low force errors lead to qualitative failures in the predicted structure. Pressure remains poorly described across most models, pointing to limitations in the density functional theory stress labels available in current large-scale training datasets. Finally, we construct an accuracy-cost Pareto frontier to identify the best trade-offs for molecular dynamics with foundation MLIPs, finding that the latest generation of cross-trained models is close to Pareto-optimal according to the accuracy metrics considered here. Overall, Dyna-Mat-v1.0 shows that end-to-end finite-temperature validation is essential for quantifying the predictive behaviour of foundation MLIPs, and provides a simple, scalable route for assessing them beyond static and harmonic benchmarks relevant to materials design.

cond-mat.mtrl-sci↗

arXiv2026

Roadmap on Advancements of the FHI-aims Software Package

Electronic-structure theory is the foundation of the description of materials including multiscale modeling of their properties and functions. Obviously, without sufficient accuracy at the base, reliable predictions are unlikely at any level that follows. The software package FHI-aims has proven to be a game changer for accurate free-energy calculations because of its scalability, numerical precision, and its efficient handling of density functional theory (DFT) with hybrid functionals and van der Waals interactions. It treats molecules, clusters, and extended systems (solids and liquids) on an equal footing. Besides DFT, FHI-aims also includes quantum-chemistry methods, descriptions for excited states and vibrations, and calculations of various types of transport. Recent advancements address the integration of FHI-aims into an increasing number of workflows and various artificial intelligence (AI) methods. This Roadmap describes the state-of-the-art of FHI-aims and advancements that are currently ongoing or planned.

cond-mat.mtrl-sci↗

arXiv2026

Journal Research Data Policies in Materials Science

Open and reproducible research in materials science relies on the availability of data, code, and common metadata standards. Journal research data policies (RDPs) remain a primary mechanism by which publication norms are defined and enforced. We survey RDPs for 171 materials science journals spanning 17 publishers, using an expanded coding framework that captures both data-and-code sharing behavior as well as refereeing standards. We find clear signs of progress in comparison to earlier research on RDPs: nearly all journals provide an RDP, and most mention data availability statements. However, enforceable requirements remain uncommon, public deposition of underlying data is rarely mandatory, and FAIR publication is typically encouraged rather than required. Expectations for research software are substantially less developed than those for data, with limited attention to versioning and persistent identifiers, dependency disclosure, reproducible execution environments, or software quality practices. Aggregating the findings on policy features into an open research data score reveals pronounced heterogeneity across journals. Neither impact factor nor access model reliably predicts policy strength. Double-coding further shows that more complex policies and stricter policies can be more challenging to interpret consistently, and we highlight challenges in consistent RDP encoding across studies. Lastly, we conclude with recommended best practice directions for the future.

cs.DL↗

arXiv2025

Vibrational Energy Dissipation in Non-Contact Single-Molecule Junctions Governed by Local Geometry and Electronic Structure

The vibrational dynamics of adsorbate molecules in single-molecule junctions depend critically on the geometric structure and electronic interactions between molecule and substrate. Vibrations, excited mechanochemically or by external stimuli, dissipate energy into substrate electrons and phonons. Energy dissipation leads to the broadening of spectral lines, vibrational lifetimes, and the coupling between molecular and substrate phonons. It affects molecular manipulation, giving rise to nanoscale friction, and contributes to scanning probe and surface spectroscopy signals. We present an approach to disentangle adsorbate vibrational dynamics in non-contact junctions by employing density functional theory, machine learning, and non-adiabatic molecular dynamics. Focusing on the CO-functionalised Cu surfaces representing a single-molecule junction, a widely studied system in scanning probe and energy dissipation experiments, we reveal strong vibrational mode specificity governed by the interplay of electron-phonon and phonon-phonon coupling. Electron-phonon relaxation rates vary by two orders of magnitude between modes and sensitively depend on the tip-substrate geometry. We find evidence of a weak non-additive effect between both energy dissipation channels, where electron-phonon coupling enhances phonon-phonon coupling. Our predicted vibrational lifetimes agree with infrared spectroscopy and helium scattering experiments. Finally, we outline how our findings can inform and enhance scanning probe experiments.

cond-mat.mes-hall↗

arXiv2025

Machine Learning and Data-Driven Methods in Computational Surface and Interface Science

Nanoscale design of surfaces and interfaces is essential for modern technologies like organic LEDs, batteries, fuel cells, superlubricating surfaces, and heterogeneous catalysis. However, these systems often exhibit complex surface reconstructions and polymorphism, with properties influenced by kinetic processes and dynamic behavior. A lack of accurate and scalable simulation tools has limited computational modeling of surfaces and interfaces. Recently, machine learning and data-driven methods have expanded the capabilities of theoretical modeling, enabling, for example, the routine use of machine-learned interatomic potentials to predict energies and forces across numerous structures. Despite these advances, significant challenges remain, including the scarcity of large, consistent datasets and the need for computational and data-efficient machine learning methods. Additionally, a major challenge lies in the lack of accurate reference data and electronic structure methods for interfaces. Density Functional Theory, while effective for bulk materials, is less reliable for surfaces, and too few accurate experimental studies on interface structure and stability exist. Here, we will sketch the current state of data-driven methods and machine learning in computational surface science and provide a perspective on how these methods will shape the field in the future.

cond-mat.mtrl-sci↗

arXiv2023

The impact of static distortion waves on superlubricity

Friction is a major source of energy loss in mechanical devices. This energy loss may be minimized by creating interfaces with extremely reduced friction, i.e. superlubricity. Conventional wisdom holds that incommensurate interface structures facilitate superlubricity. Accurately describing friction necessitates precise modeling of the interface structure. This, in turn, requires the use of accurate first-principles electronic structure methods, especially when studying organic/metal interfaces, which are highly relevant due to their tunability and propensity to form incommensurate structures. However, the system size required to calculate incommensurate structures renders such calculations intractable. As a result, studies of incommensurate interfaces have been limited to very simple model systems or strongly simplified methodology. We overcome this limitation by developing a machine-learned interatomic potential that is able to determine energies and forces for structures containing thousands to tens of thousands of atoms with an accuracy comparable to conventional first principles methods but at a fraction of the cost. Using this approach, we quantify the breakdown of superlubricity in incommensurate structures due to the formation of static distortion waves. Moreover, we extract design principles to engineer incommensurate interface systems where the formation of static distortion waves is suppressed, which facilitates low friction coefficients.

cond-mat.mtrl-sci↗

arXiv2022

The role of adatoms for the adsorption of F4TCNQ on Au(111)

Organic adlayers on inorganic substrates often contain adatoms, which can be incorporated within the adsorbed molecular species, forming two-dimensional metal-organic frameworks at the substrate surface. The interplay between native adatoms and adsorbed molecules significantly changes various adlayer properties such as the adsorption geometry, the bond strength between the substrate and the adsorbed species, or the work function at the interface. Here we use dispersion-corrected density functional theory to gain insight into the energetics that drive the incorporation of native adatoms within molecular adlayers based on the prototypical, experimentally well-characterized system of F4TCNQ on Au(111). We explain the adatom-induced modifications in the adsorption geometry and the adsorption energy based on the electronic structure and charge transfer at the interface. With this, we provide deeper insight into the general mechanisms causing the incorporation of adatoms within an adlayer made of a strong acceptor molecular species.

cond-mat.mtrl-sci↗

arXiv2021

From a bistable adsorbate to a switchable interface: tetrachloropyrazine on Pt(111)

Virtually all organic (opto)electronic devices rely on organic/inorganic interfaces with specific properties. These properties are, in turn, inextricably linked to the interface structure. Therefore, a change in structure can introduce a shift in function. If this change is reversible, it would allow constructing a switchable interface. We accomplish this with tetrachloropyrazine on Pt(111), which exhibits a double-well potential with a chemisorbed and a physisorbed minimum. These minima have significantly different adsorption geometries allowing the formation of switchable interface structures. Importantly, these structures facilitate different work function changes and coherent fractions (X-ray standing wave measurements), which are ideal properties to readout the interface state. We perform surface structure search using a modified version of the SAMPLE approach and account for thermodynamic conditions using ab-initio thermodynamics. This allows investigating millions of commensurate as well as higher-order commensurate interface structures. We identify three different classes of structures exhibiting different work function changes and coherent fractions. Using temperature and pressure as handles we demonstrate the possibility of reversible switching between those different classes, creating a dynamic interface for potential applications in organic electronics.

cond-mat.mtrl-sci↗

arXiv2021

Interfacial charge transfer influences thin-film polymorphism

The structure and chemical composition are the key parameters influencing the properties of organic thin films deposited on inorganic substrates. Such films often display structures that substantially differ from the bulk, and the substrate has a relevant influence on their polymorphism. In this work, we illuminate the role of the substrate by studying its influence for para-benzoquinone on two different substrates, Ag(111) and graphene. We employ a combination of first principles calculations and machine learning to identify the energetically most favorable structures on both substrates and study their electronic properties. Our results indicate that for the first layer, similar structures are favorable for both substrates. For the second layer we find two significantly different structures. Interestingly, graphene favors the one with less, while Ag favors the one with more electronic coupling. We explain this switch in stability as an effect of the different charge transfer on the two substrates.

cond-mat.mtrl-sci↗

arXiv2021

How much does surface polymorphism influence the work function of organic/metal interfaces?

Molecules adsorbing on metal surfaces form a variety of different surface polymorphs. How strongly this polymorphism affects interface properties is a priori unknown. In this work we investigate how strongly the surface polymorphism influences the interface work functions for various metal/organic interfaces. To evaluate the whole bandwidth of possible polymorphs, we perform full theoretical structure search, probing millions of polymorph candidates. All of these candidates might be observed in reality, either by kinetic trapping or by thermodynamic occupation. Employing first-principles calculations and machine learning we predict and analyze the work function changes for those millions of candidates for three physically distinct model systems: the weakly interacting naphthalene on Cu(111), the strongly interacting anthraquinone on Ag(111), and tetracyanoethylene, which undergoes a re-orientation from lying to standing polymorphs on the Cu(111) surface. These thorough investigations indicate that kinetic trapping of flat lying molecules can lead to work function differences of a few hundred meV. If the molecules also reorientate, this can increase to a change of several eV. We further show that the spread in work function decreases when working in thermodynamic equilibrium, but thermally occupied phases still lead to an intrinsic uncertainty at elevated temperatures.

cond-mat.mtrl-sci↗

arXiv2021

Towards targeted kinetic trapping of organic-inorganic interfaces: A computational case study

Properties of inorganic-organic interfaces, such as their interface dipole, strongly depend on the structural arrangements of the organic molecules. A prime example is tetracyanoethylene (TCNE) on Cu(111), which shows two different phases with significantly different work functions. However, the thermodynamically pre-ferred phase is not always the one that is best suited for a given application. Rather, it may be desirable to selectively grow a kinetically trapped structure. In this work, we employ density functional theory and transi-tion state theory to discuss under which conditions such a kinetic trapping might be possible for the model system of TCNE on Cu. Specifically, we want to trap the molecules in the first layer in a flat-lying orientation. This requires temperatures that are sufficiently low to suppress the re-orientation of the molecules, which is thermodynamically more favorable for high dosages, but still high enough to enable ordered growth through diffusion of molecules. Based on the temperature-dependent diffusion and re-orientation rates, we propose a temperature range at which the re-orientation can be successfully suppressed.

physics.comp-ph↗

arXiv2021

Surface Self-Assembly of Functionalized Molecules on Ag(111): More Than Just Chemical Intuition

The fabrication of nanomaterials involves self-ordering processes of functional molecules on inorganic surfaces. To obtain specific molecular arrangements, a common strategy is to equip molecules with functional groups. However, focusing on the functional groups alone does not provide a comprehensive picture. Especially at interfaces, processes that govern self-ordering are complex and involve various physical and chemical effects, often leading to structures that defy chemical intuition, as we showcase here on the example of a homologous series of quinones on Ag(111). From chemical intuition one could expect that such quinones, which all bear the same functionalization, form similar motifs. In salient contrast, our joint theoretical and experimental study shows that profoundly different structures are formed. Using a machine-learning-based structure search algorithm, we find that this is due to a shift of the balance of three antagonizing driving forces: adsorbate-substrate interactions governing adsorption sites, adsorbate-adsorbate interactions favoring close packing, and steric hindrance inhibiting certain otherwise energetically beneficial molecular arrangements. The theoretical structures show excellent agreement with our experimental characterizations of the organic/inorganic interfaces, both for the unit cell sizes and the orientations of the molecules within. With a detailed examination of all driving forces, we are further able to devise a design principle for self-assembly of functionalized molecules. The non-intuitive interplay of similarly strong interaction mechanisms will continue to be a challenging aspect for the design of functional interfaces. Our agreement between theory and experiment combined with the new physical insights indicates that these methods have now reached the necessary accuracy to do so.

cond-mat.mes-hall↗

arXiv2020

Reproducibility of Potential Energy Surfaces of Organic/Metal Interfaces on the Example of PTCDA on Ag(111)

Molecular adsorption at organic/metal interfaces depends on a range of mechanisms: covalent bonds, charge transfer, Pauli repulsion and van der Waals (vdW) interactions shape the potential energy surface (PES), making it key to understanding organic/metal interfaces. Describing such interfaces with density functional theory requires carefully selecting the exchange correlation (XC) functional and vdW correction scheme. To explore the reproducibility of the PES with respect to the choice of method, we present a benchmark of common local, semi-local and non-local XC functionals in combination with various vdW corrections. We benchmark these methods using perylenetetracarboxylic dianhydride (PTCDA) on Ag(111), one of the most frequently studied organic/metal interfaces. For each method, we determine the PES using a Gaussian process regression algorithm, which requires only about 50 DFT calculations as input. This allows a detailed analysis of the PESs' features, such as the positions and energies of minima and saddle points. Comparing the results from different combinations of XC functionals and vdW corrections enables us to identify trends and differences between the approaches. PESs for different computation methods are in qualitative agreement, but also displaying significant quantitative differences. In particular, lateral positions of adsorption geometries agree well with experiment, while adsorption heights, energies and barriers show larger discrepancies.

physics.comp-ph↗

arXiv2019

SAMPLE: Surface structure search enabled by coarse graining and statistical learning

In this publication we introduce SAMPLE, a structure search approach for commensurate organic monolayers on inorganic substrates. Such monolayers often show rich polymorphism with diverse molecular arrangements in differently shaped unit cells. Determining the different commensurate polymorphs from first principles poses a major challenge due to the large number of possible molecular arrangements. To meet this challenge, SAMPLE employs coarse-grained modeling in combination with Bayesian linear regression to efficiently map the minima of the potential energy surface. In addition, it uses ab initio thermodynamics to generate phase diagrams. Using the example of naphthalene on Cu(111), we comprehensively explain the SAMPLE approach and demonstrate its capabilities by comparing the predicted with the experimentally observed polymorphs.

cond-mat.mtrl-sci↗

arXiv2018

Leaving the Valley: Charting the Energy Landscape of Metal/Organic Interfaces via Machine Learning

The rich polymorphism exhibited by inorganic/organic interfaces is a major challenge for materials design. In this work we present a method to efficiently explore the potential energy surface and predict the formation energies of polymorphs and defects. This is achieved by training a machine learning model on a list of only 100 candidate structures that are evaluated via dispersion-corrected Density Functional Theory (DFT) calculations. We demonstrate the power of this approach for tetracyanoethylene on Ag(100) and explain the anisotropic ordering that is observed experimentally.

cond-mat.mtrl-sci↗