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Karsten Reuter

Publications and source records attributed to Karsten Reuter.

At least 19 recordsLinked to original sources
arXiv2026

Electrostatic Phenomenology Benchmarks for Machine-Learned Interatomic Potentials in Electrochemistry: Beyond the Energy-Force Metric

Accurate treatment of long-range interactions in machine learning interatomic potentials (MLIPs) is essential for electrochemical simulations. However, aggregate energy and force errors alone are insufficient to establish an MLIP's physical accuracy since they do not detect qualitative inconsistencies in the model such as the prediction of image-charge attraction, dielectric screening, or charge transfer. We introduce a benchmark suite EPhEct (Electrostatic Phenomena for Electrochemistry) of focused test cases designed to evaluate MLIPs on electrochemically relevant physical phenomena. The tests probe for image-charge attraction at a metal electrode, the splitting between longitudinal and transverse optical phonons as a probe of ionic and electronic screening, the dipole moment of interfacial water, and Fermi-level pinning during ion discharge. These tests establish a qualitative diagnostic routine complementary to aggregate energy-force metrics.

cond-mat.mtrl-sci↗
arXiv2026

Finite-Temperature Thermodynamics of Cu(100) Oxidation: Missing-Row Reconstruction, Defect States, and Order-Disorder Transition from Nested Sampling

Metal surfaces undergo structural, compositional, and morphological changes in response to their chemical environment. Tuning the surfaces' function and stability for a given application correspondingly necessitates an understanding of how this surface evolution couples to external conditions. Here, we demonstrate the feasibility of nested sampling simulations to obtain this coupling at first-principles predictive quality. By exploring the full configuration space, nested sampling estimates the partition function and gives direct access to desired thermodynamic ensemble averages at any temperature without prior knowledge. Computational feasibility is achieved through machine-learned interatomic potentials, an efficient GPU implementation of the sampling algorithm and bespoke sampling moves. Applied to the early oxidation of Cu(100), the approach successfully predicts the experimentally observed, complex $(2\sqrt{2}\times\sqrt{2})$R45$^\circ$-O missing-row reconstruction. The full access to the partition function enables a detailed characterization of the temperature-dependent surface evolution, mapping the emergence of defect states and the order-disorder transition of the reconstructed surface.

cond-mat.mtrl-sci↗
arXiv2026

Learning to Converge: Warm-Starting DFTB Self-Consistent Charges with Machine Learning

Semiempirical electronic structure methods such as Density-Functional Tight-Binding (DFTB) offer a computationally efficient approach to molecular and materials simulations, bridging the gap between first-principles accuracy and classical force field speed while retaining full access to electronic properties. However, DFTB calculations based on self-consistent charge (SCC) schemes can still suffer from slow convergence, particularly for complex molecular and materials systems, making the iterative procedure a significant bottleneck in large-scale simulations and high-throughput workflows. We present a machine learning approach that accelerates DFTB simulations by predicting optimal initial atomic charges. Using element-specific models based on the Smooth Overlap of Atomic Positions descriptor and kernel ridge regression, we train charge models on reference calculations and demonstrate that ML-predicted initial charges consistently and significantly improve SCC convergence across diverse chemical systems including organic molecules, biomolecules, water clusters, transition metal oxides and solid electrolytes.

cond-mat.mtrl-sci↗
arXiv2026

General-purpose LLMs as Constrained Crystal Composition Generators

The targeted discovery of inorganic materials remains challenging due to the vastness of compositional design spaces and the high cost of exhaustive screening. Task-specific generative artificial intelligence represents a particularly efficient alternative to screening, yet demands tedious collection of training data before providing real benefit. General-purpose large language models (LLMs) have recently shown tremendous potential for the targeted generation of single, optimal materials compositions without the need for task-specific fine-tuning. However, it is unclear whether LLMs generally pose an advantage compared to specialized generative models, in particular in large design spaces. Here, we demonstrate that such models are capable of covering entire regions of the targeted property space effectively and systematically. Using Elpasolite materials as an established benchmark for generative tasks in large chemical spaces, we find that an iterative prompt-and-response framework is able to recover on average 96% of all low-energy Elpasolites in the target region. This performance, driven mainly by iterative in-context learning, surpasses the generative abilities of previous, task-specific models. Our results establish general-purpose LLMs as flexible and accessible components for inverse materials design workflows.

cond-mat.mtrl-sci↗
arXiv2026

Adaptive Slater Koster Parameters: Crossing Oxidation States with Density Functional Tight Binding

We propose to adapt the confined pseudo-atomic orbitals underpinning the precalculated Slater-Koster (SK) interaction tables in Density Functional Tight Binding (DFTB) to local atomic environments. We demonstrate significant improvement in electronic structure and energetics in the application to a partially oxidized Ni surface and Li insertion into graphite, where we assign optimal SK parameters to metal atoms in different oxidation states. Further analysis reveals the smoothness of the SK integrals across the varying oxidation states. Exploiting this, we introduce a site-resolved machine-learning scheme for fully adaptive DFTB. Using atomic descriptors and simple regression architectures already established in the context of machine-learning interatomic potentials, our scheme achieves 95% band-structure accuracy across all Ni-O binary compositions in the Materials Project.

cond-mat.mtrl-sci↗
arXiv2025

Coupling all-electron full-potential density functional theory with grid-based continuum embeddings

Recent advances in continuum embedding models have enabled the incorporation of solvent and electrolyte effects into density functional theory (DFT) simulations of material surfaces, significantly benefiting electrochemistry, catalysis, and other applications. To extend the simulation of diverse systems and properties, the implementation of continuum embedding models into the Environ library adopts a modular programming paradigm, offering a flexible interface for communication with various DFT programs. The speed and scalability of the current implementation rely on a smooth definition of the key physical properties of the atomistic system, in particular of its electronic density. This has hindered the coupling of Environ with all-electron simulation packages, as the sharp electron density peaks near atomic nuclei are difficult to represent on regular grids. In this work, we introduce a novel smoothing scheme that transforms atom-centered electron densities into a regular grid representation while preserving the accuracy of electrostatic calculations. This approach enables a minimal and generic interface, facilitating seamless interoperability between Environ and all-electron DFT programs. We demonstrate this development through the coupling of Environ with the FHI-aims package and present benchmark simulations that validate the proposed method.

physics.comp-ph↗
arXiv2025

A foundation model for atomistic materials chemistry

Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early ML force fields have largely been limited by: (i) the substantial computational and human effort of developing and validating potentials for each particular system of interest; and (ii) a general lack of transferability from one chemical system to the next. Here we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model - and its qualitative and at times quantitative accuracy - on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces and even the dynamics of a small protein. The model can be applied out of the box as a starting or "foundation" model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users get reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step towards democratising the revolution in atomic-scale modeling that has been brought about by ML force fields.

physics.chem-ph↗
arXiv2025

Multiscale Microscopy via Automation: Dual Magnification ESEM Imaging by Frame Alternation

In Environmental Scanning Electron Microscopy (ESEM) experiments, the acquisition parameters are generally kept constant throughout the collection of a data set. This limits data collection to one data set at a time, and frequent human interaction is required to maintain the image quality. Here, we use a custom-designed automation interface to minimize such supervision and allow for the collection of multiple interlaced data sets simultaneously. The oscillatory modes of an example catalytic system (hydrogen oxidation over Co foil) were employed as a tunable spatiotemporal test case. Using our automation interface, we can implement more advanced acquisition programs into the microscope that allow dual imaging - effectively bridging reaction monitoring between different length scales. By using automation to change the settings of the acquisition after each frame, we are able to capture alternating magnifications of the same process and sample location. Both a low magnification overview of the mesoscopic surface dynamics and a high magnification field of view of the ongoing structural changes of a selected surface motif were acquired. Using such truly correlative data captured with the dual magnification method, cross-scale correlations about catalytic systems including phase information can be established.

physics.ins-det↗
arXiv2025

Spectral tuning of hyperbolic shear polaritons in monoclinic gallium oxide via isotopic substitution

Hyperbolic phonon polaritons - hybridized modes arising from the ultrastrong coupling of infrared light to strongly anisotropic lattice vibrations in uniaxial or biaxial polar crystals - enable to confine light to the nanoscale with low losses and high directionality. In even lower symmetry materials, such as monoclinic $β$-Ga$_2$O$_3$ (bGO), hyperbolic shear polaritons (HShPs) further enhance the directionality. Yet, HShPs are intrinsically supported only within narrow frequency ranges defined by the phonon frequencies of the host material. Here, we report spectral tuning of HShPs in bGO by isotopic substitution. Employing near-field optical microscopy to image HShPs in $^{18}$O bGO films homo-epitaxially grown on a $^{16}$O bGO substrate, we demonstrate a spectral redshift of $\sim~40~$cm$^{-1}$ for the $^{18}$O bGO, compared to $^{16}$O bGO. The technique allows for direct observation and a model-free estimation of the spectral shift driven by isotopic substitution without the need for knowledge of the dielectric tensor. Complementary far-field measurements and ab initio calculations - in good agreement with the near-field data - confirm the effectiveness of this estimation. This multifaceted study demonstrates a significant isotopic substitution induced spectral tuning of HShPs into a previously inaccessible frequency range, creating new avenues for technological applications of such highly directional polaritons.

physics.optics↗
arXiv2025

On Boltzmann Averaging in Ab Initio Thermodynamics

Ab initio thermodynamics is a widespread, computationally efficient approach to predict the stable configuration of a surface in contact with a surrounding (gas or liquid) environment. In a prevalent realization of this approach, this stable configuration is simply equated with the structure in a considered candidate pool that exhibits the lowest surface free energy. Here we discuss the possibility to consider the thermal accessibility of competing, higher-energy configurations through Boltzmann averaging when the extended surface configurations and their energetics are computed within periodic boundary condition supercells. We show analytically that fully converged averages can be obtained with a candidate pool derived from exhaustive sampling in a surface unit-cell exceeding the system's correlation length. In contrast, averaging over a small pool of ad hoc assembled structures is generally ill-defined. Enumerations of a lattice-gas Hamiltonian model for on-surface oxygen adsorption at Pd(100) are employed to illustrate these considerations in a practical context.

physics.chem-ph↗
arXiv2025

Machine Learning the Energetics of Electrified Solid/Liquid Interfaces

We present a response-augmented machine learning (ML) approach to the energetics of electrified metal surfaces. We leverage local descriptors to learn the work function as the first-order energy change to introduced bias charges and stabilize this learning through Born effective charges. This permits the efficient extension of ML interatomic potential architectures to include finite bias effects up to second-order. Application to OH at Cu(100) rationalizes the experimentally observed pH-dependence of the preferred adsorption site in terms of a non-Nernstian charge-induced site switching.

cond-mat.mtrl-sci↗
arXiv2025

Full Crystallographic Imaging of Hexagonal Boron Nitride Monolayers with Phonon-Enhanced Sum-Frequency Microscopy

Hexagonal boron nitride (hBN) is an important 2D material for van der Waals heterostructures, single photon emitters, and infrared nanophotonics. The optical characterization of mono- and few-layer samples of hBN however remains a challenge as the material is almost invisible optically. Here we introduce phase-resolved sum-frequency microscopy as a technique for imaging monolayers of hBN grown by chemical vapor deposition (CVD) and visualize their crystal orientation. A combination of femtosecond mid-infrared (IR) and visible laser pulses is used for sum-frequency generation (SFG), which is imaged in a wide-field optical microscope. The IR laser resonantly excites a phonon of hBN that leads to an ~800-fold enhancement of the SFG intensity, making it possible to image large 100x100 μm2 sample areas in less than 1 s. Implementing heterodyne detection in combination with azimuthal rotation of the sample further provides full crystallographic information. Through combined knowledge of topography and crystal orientation, we find that triangular domains of CVD-grown monolayer hBN have nitrogen-terminated zigzag edges. Overall, SFG microscopy can be used as an ultra-sensitive tool to image crystal structure, strain, stacking sequences, and twist angles, and is applicable to the wide range of van der Waals structures, where location and identification of monolayer regions and interfaces with broken inversion symmetry is of paramount importance.

cond-mat.mtrl-sci↗
arXiv2025

Advancing our Understanding of Optoionic Effects for the Design of Solar Batteries: A Theoretical Perspective

Optoionics, a promising new field that aims at controlling ion dynamics using light, links photovoltaic power generation with electrochemical charge storage. This has the potential to drive and accelerate the energy revolution by utilizing materials that integrate the functionality of batteriesand photovoltaic cells. Finding, optimizing, and customizing these materials is a complex task, though. Computational modeling can play a crucial role in guiding and speeding up these processes, particularly when the atomic mechanisms are not well understood. This does however require expertise in various areas, including advanced electronic-structure theory, machine learning, and multi-scale approaches. In this perspective, we shed light on the intricacies of modeling optoionic effects for solar battery materials. We first discuss the underlying physical and chemical mechanisms, as well as the computational tools that are available to date for describing these processes. Furthermore, we discuss the limits of these approaches and identify key challenges that need to be tackled to advance this field.

cond-mat.mtrl-sci↗
arXiv2024

Operando Characterization and Molecular Simulations Reveal the Growth Kinetics of Graphene on Liquid Copper during Chemical Vapor Deposition

In recent years, liquid metal catalysts have emerged as a compelling choice for the controllable, large-scale, and high-quality synthesis of two-dimensional materials. At present, there is little mechanistic understanding of the intricate catalytic process, though, of its governing factors or what renders it superior to growth at the corresponding solid catalysts. Here, we report on a combined experimental and computational study of the kinetics of graphene growth during chemical vapor deposition on a liquid copper catalyst. By monitoring the growing graphene flakes in real time using in situ radiation-mode optical microscopy, we explore the growth morphology and kinetics over a wide range of CH4-to-H2 pressure ratios and deposition temperatures. Constant growth rates of the flakes' radius indicate a growth mode limited by precursor attachment, whereas methane-flux-dependent flake shapes point to limited precursor availability. Large-scale free energy simulations enabled by an efficient machine-learning moment tensor potential trained to density-functional theory data provide quantitative barriers for key atomic-scale growth processes. The wealth of experimental and theoretical data can be consistently combined into a microkinetic model that reveals mixed growth kinetics that, in contrast to the situation at solid Cu, is partly controlled by precursor attachment alongside precursor availability. Key mechanistic aspects that directly point toward the improved graphene quality are a largely suppressed carbon dimer attachment due to the facile incorporation of this precursor species into the liquid surface and a low-barrier ring-opening process that self-heals 5-membered rings resulting from remaining dimer attachments.

cond-mat.mtrl-sci↗
arXiv2023

Converging Divergent Paths: Constant Charge vs. Constant Potential Energetics in Computational Electrochemistry

Using the example of a proton adsorption process, we analyze and compare two prominent modelling approaches in computational electrochemistry at metallic electrodes - electronically canonical, constant-charge and electronically grand-canonical, constant-potential calculations. We first confirm that both methodologies yield consistent results for the differential free energy change in the infinite cell size limit. This validation emphasizes that, fundamentally, both methods are equally valid and precise. In practice, the grand-canonical, constant-potential approach shows superior interpretability and size convergence as it aligns closer to experimental ensembles and exhibits smaller finite-size effects. On the other hand, constant-charge calculations exhibit greater resilience against discrepancies, such as deviations in interfacial capacitance and absolute potential alignment, as their results inherently only depend on the surface charge, and not on the modelled charge vs. potential relation. The present analysis thus offers valuable insights and guidance for selecting the most appropriate ensemble when addressing diverse electrochemical challenges.

physics.chem-ph↗
arXiv2023

Ab-Initio-Based Modeling of Thermodynamic Cyclic Voltammograms: A Benchmark Study on Ag(100) in Bromide Solutions

Experimental cyclic voltammograms (CVs) measured in the slow scan rate limit can be entirely described in terms of thermodynamic equilibrium quantities of the electrified solid-liquid interface. They correspondingly serve as an important benchmark for the quality of first-principles calculations of the interfacial thermodynamics. Here, we investigate the partially drastic approximations made presently in computationally efficient such calculations for the well-defined showcase of a Ag(100) model electrode in Br-containing electrolytes, where the non-trivial part of the CV stems from the electrosorption of Br ions. We specifically study the entanglement of common approximations in the treatment of solvation and field effects, as well as in the way macroscopic averages of the two key quantities, namely the potential-dependent adsorbate coverage and electrosorption valency, are derived from the first-principles energetics. We demonstrate that the combination of energetics obtained within an implicit solvation model and a perturbative second order account of capacitive double layer effects with a constant-potential grand-canonical Monte Carlo sampling of the adsorbate layer provides an accurate description of the experimental CV. However, our analysis also shows that error cancellation at lower levels of theory may equally lead to good descriptions even though key underlying physics like the disorder-order transition of the Br adlayer at increasing coverages is inadequately treated.

physics.chem-ph↗
arXiv2023

wfl Python Toolkit for Creating Machine Learning Interatomic Potentials and Related Atomistic Simulation Workflows

Predictive atomistic simulations are increasingly employed for data intensive high throughput studies that take advantage of constantly growing computational resources. To handle the sheer number of individual calculations that are needed in such studies, workflow management packages for atomistic simulations have been developed for a rapidly growing user base. These packages are predominantly designed to handle computationally heavy ab initio calculations, usually with a focus on data provenance and reproducibility. However, in related simulation communities, e.g. the developers of machine learning interatomic potentials (MLIPs), the computational requirements are somewhat different: the types, sizes, and numbers of computational tasks are more diverse, and therefore require additional ways of parallelization and local or remote execution for optimal efficiency. In this work, we present the atomistic simulation and MLIP fitting workflow management package wfl and Python remote execution package ExPyRe to meet these requirements. With wfl and ExPyRe, versatile Atomic Simulation Environment based workflows that perform diverse procedures can be written. This capability is based on a low-level developer-oriented framework, which can be utilized to construct high level functionality for user-friendly programs. Such high level capabilities to automate machine learning interatomic potential fitting procedures are already incorporated in wfl, which we use to showcase its capabilities in this work. We believe that wfl fills an important niche in several growing simulation communities and will aid the development of efficient custom computational tasks.

physics.comp-ph↗
arXiv2023

A Theoretical Investigation of the Grand- and the Canonical Potential Energy Surface: The Interplay between Electronic and Geometric Response at Electrified Interfaces

How does an electrochemical interface respond to changes in the electrode potential? How does the response affect the key properties of the system - energetics, excess charge, capacitance? Essential questions key to ab-initio simulations of electrochemical systems, which we address in this work on the basis of a rigorous mathematical evaluation of the interfacial energetics at constant applied potential. By explicitly taking into account the configurational and electronic degrees of freedom we derive important statements about stationary points in the electronically grand canonical ensemble. We analyze their geometric response to changes in electrode potential and show that it can be mapped identically onto an additional contribution to the system's capacitance. We draw similar conclusions for the constant charge ensemble which equally allows to assess the respective stationary points. Our analysis of the relation between the canonical and grand canonical energetics reveals, however, one key difference between both ensembles. While the constant potential ensemble yields in general positive capacitances at local minima, the capacitance of local minima in the constant charge ensemble might become negative. We trace back this feature to the possibility of character switching of stationary points when switching between the ensembles causing the differences in the response to perturbations. Our systematical analysis not only provides a detailed qualitative and quantitative understanding of the interplay between electronic and configurational degrees of freedom and their contributions to the energetics of electrified interfaces but also highlights the similarities and subtle dissimilarities between the canonical and grand canonical description of the electronic degrees of freedom, which is crucial for a better understanding of theoretical calculations with and without potentiostat.

physics.chem-ph↗