arXiv subjects

Reinhard J. Maurer

Publications and source records attributed to Reinhard J. Maurer.

At least 19 recordsLinked to original sources

arXiv2026

Long-Range Machine Learning Interatomic Potentials for Defect Energetics in SrTiO$_3$

Machine learning interatomic potentials (MLIPs) have advanced rapidly in recent years, yet the majority of models remain semilocal in nature and neglect long-range electrostatic interactions. A growing number of long-range models have emerged to address this limitation, but systematic benchmarks comparing their performance on physically realistic systems remain scarce. In this work, we assess three long-range MLIPs -- MACE-POLAR, MACE-LES, and LOREM, against a short-range baseline MACE model, on their ability to capture long-range Coulombic interactions, progressing from idealised synthetic point-charge systems to first-principles data on realistic crystal structures. This progression allows us to isolate model's capability to learn electrostatic interactions from the many competing energy contributions, such as dielectric screening and exchange-correlation, that are present in DFT total energies. As a physically motivated benchmark system, we focus on strontium titanate SrTiO$_3$ with Sr--O Schottky vacancy pairs, a material in which long-range charge interactions play a central role and which serves as a natural stepping stone towards the study of charged polarons and other electronic and optical properties. Building on this benchmark, we further examine oxygen vacancy migration in the presence of the Sr--O Schottky pair, using climbing-image nudged elastic band calculations and molecular dynamics to obtain activation barriers as a function of the vacancy pair separation, providing further extrapolation test of each model's ability to capture the electrostatic effects that govern defect transport.

cond-mat.mtrl-sci↗

arXiv2026

Role of anisotropic electronic friction in laser-driven hydrogen recombination on copper

Ultrafast light-driven chemical dynamics at surfaces are governed by energy transfer from excited electrons to vibrational degrees of freedom. When this nonadiabatic energy transfer is anisotropic, it can lead to dynamical steering effects that affect reaction probabilities or non-thermal final energy distributions in molecules. Here, enabled by a machine-learning-based simulation framework, we compare isotropic and anisotropic models of electronic friction during laser-driven hydrogen evolution on the (111) facet of copper. While anisotropic electronic friction determines the rate of energy transfer into the adsorbate and the fluence dependence of desorption probabilities, final translational, vibrational and rotational energy distributions are mainly governed by the potential energy landscape at the barrier and insensitive to nonadiabatic effects.

physics.chem-ph↗

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

Memory-dependent electronic friction for nonadiabatic dynamics at metal surfaces

Electronic excitation induced by nuclear motion is a key energy dissipation channel in chemical dynamics at metal surfaces. Here, nonadiabatic effects can be treated via molecular dynamics with electronic friction, where they act as frictional drag and fluctuation force contributions. Commonly, the Markov approximation is imposed, so memory effects are ignored. A theoretical formalism is presented to evaluate tensorial and configuration-dependent electronic friction memory kernels from first principles. We evaluate friction kernels for Newns--Anderson Hamiltonian models as well as within Kohn--Sham density functional theory and analyse their mathematical properties and configuration dependence. For hyperthermal atomic and diatomic scattering, memory effects arising from frequency and configuration dependence of electronic friction affect energy exchange between adsorbate and metal electrons. Memory effects lead to an increase of vibrational and a reduction of translational energy loss in the case of nitric oxide scattering on Au(111), leading to an increase of directional anisotropy of friction. Importantly, memory-dependent evaluation of electronic friction removes the need to define a single effective Markovian friction coefficient from the structured frequency-dependent electronic response.

cond-mat.mtrl-sci↗

arXiv2026

Equivariant Electronic Hamiltonian Prediction with Many-Body Message Passing

Machine learning surrogate models of Kohn-Sham Density Functional Theory Hamiltonians provide a powerful tool for accelerating the prediction of electronic properties of materials, such as electronic band structures and density of states. For large-scale applications, an ideal model would exhibit high generalization ability and computational efficiency. Here, we introduce the MACE-H graph neural network, which combines high body-order message passing with a node-order expansion to efficiently obtain all relevant $O(3)$ irreducible representations. The model achieves high accuracy and computational efficiency and captures the full local chemical environment features of, currently, up to $f$ orbital matrix interaction blocks. We demonstrate the model's accuracy and transferability on several open materials benchmark datasets of two-dimensional materials and a new dataset for bulk gold, achieving sub-meV prediction errors on matrix elements and high accuracy on eigenvalues across all systems. We further analyze the interplay of high-body-order message passing and locality that makes this model a good candidate for high-throughput material screening.

cond-mat.mtrl-sci↗

arXiv2026

Mode selectivity in electron promoted vibrational relaxation of chemisorbed hydrogen on molybdenum and tungsten surfaces

Electron-phonon coupling in atoms and molecules adsorbed at metal surfaces gives rise to finite vibrational linewidths in infrared or electron energy loss spectra. When it is the dominant contribution to the vibrational lifetime, it manifests itself in the form of a Fano line shape. Here, we report the linewidths of vibrational modes of chemisorbed hydrogen on the (100) and (110) surfaces of molybdenum and tungsten calculated from first-order time-dependent perturbation theory. For those modes with a Fano line shape, our results are in good agreement with the experiment. We further observe that the coupling strength between vibrations and electrons depends on the nature of the mode: for Lorentzian-shaped peaks, the experimental linewidths are always larger than those predicted based on pure electron-phonon coupling. The calculated linewidths exhibit a strong coverage dependence, decreasing towards higher coverages. This finding has important implications for nonadiabatic energy dissipation in hydrogen dynamics at metal surfaces. While electron-hole pair excitation is the dominant energy-transfer mechanism between hydrogen and pristine metal surfaces, other channels for energy dissipation, such as adsorbate-adsorbate interactions, may become more significant on metal surfaces densely covered with hydrogen.

cond-mat.mtrl-sci↗

arXiv2026

First-principles Newns-Anderson Hamiltonian Construction for Chemisorbed Hydrogen at Metal Surfaces

The Newns-Anderson Hamiltonian is widely used to describe adsorption at gas-solid interfaces, yet its construction typically relies on simplifying assumptions such as constant coupling and the wideband limit approximation. Here, we present a first-principles approach to construct Newns-Anderson Hamiltonians by applying projection operator diabatisation to Hamiltonian matrices obtained from Kohn-Sham density functional theory calculations. We demonstrate this method for chemisorbed hydrogen on three fcc metal(111) surfaces: Al, Cu, and Pt. To validate the electronic coupling between adsorbed hydrogen and the metal surface, we compute the projected density of states, electronic tunnelling lifetimes, and vibrational lifetimes from the constructed Newns-Anderson Hamiltonians and find good agreement with reference calculations. Analysis of the chemisorption function reveals that the wideband limit approximation is valid for H/Al(111) but has limited applicability for H/Cu(111) and H/Pt(111).

cond-mat.mtrl-sci↗

arXiv2025

A Haldane-Anderson Hamiltonian Model for Hyperthermal Hydrogen Scattering from a Semiconductor Surface

Collisions of atoms and molecules with metal surfaces create electronic excitations in the metal, leading to nonadiabatic energy dissipation, inelastic scattering, and sticking. Mixed quantum-classical molecular dynamics simulation methods, such as molecular dynamics with electronic friction, are able to capture nonadiabatic energy loss during dynamics at metal surfaces. Hydrogen atom scattering from semiconductors, on the other hand, exhibits strong adsorbate-surface energy transfer only when the projectile kinetic energy exceeds the bandgap of the substrate. Electronic friction fails to describe this effect. Here, we report a first-principles parameterization of a simple Haldane-Anderson Hamiltonian model of hydrogen atom gas-surface scattering on Ge(111)$c(2\times8)$, for which hyperthermal scattering experiments have been reported. We subsequently perform independent-electron surface hopping and Ehrenfest dynamics simulations on this model, and validate these results through numerically exact quantum-dynamical simulations using the hierarchical equation of motion approach. While mean-field dynamics yield weak nonadiabatic energy loss that is independent of the initial kinetic energy, independent electron surface hopping simulations qualitatively agree with the experimental observation that nonadiabatic energy dissipation only occurs if the initial kinetic energy exceeds the bandgap of the surface.

cond-mat.mtrl-sci↗

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

Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning

Machine-learned interatomic potentials are revolutionising atomistic materials simulations by providing accurate and scalable predictions within the scope covered by the training data. However, generation of an accurate and robust training data set remains a challenge, often requiring thousands of first-principles calculations to achieve high accuracy. Foundation models have started to emerge with the ambition to create universally applicable potentials across a wide range of materials. While foundation models can be robust and transferable, they do not yet achieve the accuracy required to predict reaction barriers, phase transitions, and material stability. This work demonstrates that foundation model potentials can reach chemical accuracy when fine-tuned using transfer learning with partially frozen weights and biases. For two challenging datasets on reactive chemistry at surfaces and stability and elastic properties of tertiary alloys, we show that frozen transfer learning with 10-20% of the data (hundreds of datapoints) achieves similar accuracies to models trained from scratch (on thousands of datapoints). Moreover, we show that an equally accurate, but significantly more efficient surrogate model can be built using the transfer learned potential as the ground truth. In combination, we present a simulation workflow for machine learning potentials that improves data efficiency and computational efficiency.

cond-mat.mtrl-sci↗

arXiv2025

Generative design of functional organic molecules for terahertz radiation detection

Plasmonic nanocavities are molecule-nanoparticle junctions that offer a promising approach to upconvert terahertz radiation into visible or near-infrared light, enabling nanoscale detection at room temperature. However, the identification of molecules with strong terahertz-to-visible frequency upconversion efficiency is limited by the availability of suitable compounds in commercial databases. Here, we employ the generative autoregressive deep neural network, G-SchNet, to perform property-driven design of novel monothiolated molecules tailored for terahertz radiation detection. To design functional organic molecules, we iteratively bias G-SchNet to drive molecular generation towards highly active and synthesizable molecules based on machine learning-based property predictors, including molecular fingerprints and state-of-the-art neural networks. We study the reliability of these property predictors for generated molecules and analyze the chemical space and properties of generated molecules to identify trends in activity. Finally, we filter generated molecules and plan retrosynthetic routes from commercially available reactants to identify promising novel compounds and their most active vibrational modes in terahertz-to-visible upconversion.

physics.chem-ph↗

arXiv2025

Challenges in the Theory and Atomistic Simulation of Metal Electrodeposition

Electrodeposition is a fundamental process in electrochemistry, and has applications in numerous industries, such as corrosion protection, decorative finishing, energy storage, catalysis, and electronics. While there is a long history of using electrodeposition, its application for controlled nanostructure growth is limited. The establishment of an atomic-scale understanding of the electrodeposition process and dynamics is crucial to enable the controlled fabrication of metal nanoparticles and other nanostructures. Significant advancements in molecular simulation capabilities and the electronic structure theory of electrified solid-liquid interfaces bring theory closer to realistic applications, but a gap remains between realistic applications, theoretical understanding of dynamics, and atomistic simulation. In this review we briefly summarize the current state-of-the-art computational techniques available for the simulation of electrodeposition and electrochemical growth on surfaces, and identify the remaining open challenges.

cond-mat.mtrl-sci↗

arXiv2025

Nonadiabatic reactive scattering of hydrogen on different surface facets of copper

Dissociative chemisorption is a key process in hydrogen-metal surface chemistry, where nonadiabatic effects due to low-lying electron-hole-pair excitations may affect reaction outcomes. Molecular dynamics with electronic friction simulations can capture weak nonadiabatic effects at metal surfaces, but require as input energy landscapes and electronic friction tensors. Here, we present full-dimensional machine learning surrogate models of the electronic friction tensor to study reactive hydrogen chemistry at the low-index surface facets Cu(100), Cu(110), Cu(111), and Cu(211). We combine these surrogate models with machine learning interatomic potentials to simulate quantum-state-resolved H$_2$ reactive scattering on pristine copper surfaces. The predicted sticking coefficient and survival probabilities are in excellent agreement with experiment. Comparison between adiabatic and nonadiabatic simulations reveals that the influence of electron-hole pair excitations on the scattering dynamics is weak and that the probability for dissociative adsorption is dominated by the shape of the underlying potential energy surface and the initial vibrational quantum state. Nonadiabatic effects only lead to subtle changes in rovibrationally inelastic state-to-state scattering probabilities. The differences between jellium-based isotropic local density friction and full ab-initio response-theory-based orbital-dependent friction are even more subtle. The presented machine learning models represent the most accurate publicly available, full-dimensional models for H$_2$ on copper to date.

physics.chem-ph↗

arXiv2025

Understanding and improving transferability in machine-learned activation energy predictors

The calculation of reactive properties is a challenging task in chemical reaction discovery. Machine learning (ML) methods play an important role in accelerating electronic structure predictions of activation energies and reaction enthalpies, and are a crucial ingredient to enable large-scale automated reaction network discovery with $>10^3$ reactions. Unfortunately, the predictive accuracy of existing ML models does not yet reach the required accuracy across the space of possible chemical reactions to enable subsequent kinetic simulations that even qualitatively agree with experimental kinetics. Here, we comprehensively assess the underlying reasons for prediction failures within a selection of machine-learned models of reactivity. Models based on difference fingerprints between reactant and product structures lack transferability despite providing good in-distribution predictions. This results in a significant loss of information about the context and mechanism of chemical reactions. We propose a convolutional ML model that uses atom-centered quantum-chemical descriptors and approximate transition state information. Inclusion of the latter improves transferability for out-of-distribution benchmark reactions, making greater use of the limited chemical reaction space spanned by the training data. The model further delivers atom-level contributions to activation energies and reaction enthalpies that provide a useful interpretational tool for rationalizing reactivity.

physics.chem-ph↗

arXiv2025

Structural bias in three-dimensional autoregressive generative machine learning of organic molecules

A range of generative machine learning models for the design of novel molecules and materials have been proposed in recent years. Models that can generate three-dimensional structures are particularly suitable for quantum chemistry workflows, enabling direct property prediction. The performance of generative models is typically assessed based on their ability to produce novel, valid, and unique molecules. However, equally important is their ability to learn the prevalence of functional groups and certain chemical moieties in the underlying training data, that is, to faithfully reproduce the chemical space spanned by the training data. Here, we investigate the ability of the autoregressive generative machine learning model G-SchNet to reproduce the chemical space and property distributions of training datasets composed of large, functional organic molecules. We assess the elemental composition, size- and bond-length distributions, as well as the functional group and chemical space distribution of training and generated molecules. By principal component analysis of the chemical space, we find that the model leads to a biased generation that is largely unaffected by the choice of hyperparameters or the training dataset distribution, producing molecules that are, on average, more unsaturated and contain more heteroatoms. Purely aliphatic molecules are mostly absent in the generation. We further investigate generation with functional group constraints and based on composite datasets, which can help partially remedy the model generation bias. Decision tree models can recognize the generation bias in the models and discriminate between training and generated data, revealing key chemical differences between the two sets. The chemical differences we find affect the distributions of electronic properties such as the HOMO-LUMO gap, which is a common target for functional molecule design.

physics.chem-ph↗

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↗

arXiv2025

Density-Functional Perturbation Theory with Numeric Atom-Centered Orbitals

This paper represents one contribution to a larger Roadmap article reviewing the current status of the FHI-aims code. In this contribution, the implementation of density-functional perturbation theory in a numerical atom-centered framework is summarized. Guidelines on usage and links to tutorials are provided.

cond-mat.mtrl-sci↗

arXiv2024

Room Temperature Hydrogen Atom Scattering Experiments Are Not a Sufficient Benchmark to Validate Electronic Friction Theory

In the dynamics of atoms and molecules at metal surfaces, electron-hole pair excitations can play a crucial role. In the case of hyperthermal hydrogen atom scattering, they lead to nonadiabatic energy loss and highly inelastic scattering. Molecular dynamics with electronic friction simulations where friction is computed under an isotropic homogeneous electron gas approximation have previously shown good agreement with measured kinetic energy loss distributions, suggesting that this level of theoretical description is sufficient to describe nonadiabatic effects of atomic scattering. In this work, we show that similar agreement with room temperature experiments can also be achieved with friction derived from density functional theory linear response calculations. The apparent agreement of the homogeneous electron gas approximation with experiment arises from a fortuitous cancellation of errors where friction is overestimated close to the surface and the spin transition is neglected. Only for scattering at low temperatures can both approximations be distinguished and differences rationalised in terms of the number of bounces of the atom on the surface. We identify the signatures of nonadiabatic energy loss of different levels of theory, which future low-temperature scattering experiments will be able to measure.

cond-mat.mtrl-sci↗