arXiv subjects

Jörg Neugebauer

Publications and source records attributed to Jörg Neugebauer.

At least 19 recordsLinked to original sources

arXiv2026

Fully Compensated Lines in Ferrimagnets

We generalize the classic Néel diagram and identify another type of ferrimagnetic phase that remains fully magnetically compensated below the Curie temperature, forming a continuous line of compensated points. It exhibits zero net magnetization while retaining non-relativistic, eV-scale reciprocal spin splitting. We further find a persistently enhanced intrinsic switching field over a broad temperature range near the fully compensated phase. Proximity to the compensated line is achieved by minimizing the net local moment while balancing exchange interactions with respect to the number of equivalent atoms in each sublattice. The resulting extended Néel diagram provides practical design rules for engineering fully compensated ferrimagnetic phases via targeted chemical substitution that combines atoms with robust and weak local moments, as demonstrated through density functional theory and Monte Carlo simulations for GdCo$_5$-type ferrimagnets.

cond-mat.mtrl-sci↗

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

Computing binary alloy phase diagrams with explicit configurational and vibrational entropy

Phase stability in multicomponent solid solutions depends on configurational entropy beyond the ideal mixing limit, but capturing it together with vibrational entropy within the same atomistic framework remains challenging. Here, we extend non-equilibrium thermodynamic integration to composition-dependent transformations through an alchemical interpolation of the interactions, combined with Monte Carlo identity exchange moves and molecular dynamics that sample the vibrational and non-ideal configurational entropy along the integration path. We apply the framework to the Au-Cu binary alloy using Atomic Cluster Expansion potentials trained on density functional theory data using the LDA, PBE, and r2SCAN functionals, and construct composition-temperature phase diagrams directly from atomistic free energies. We find that explicit configurational sampling lowers the AuCu order-disorder transition temperature predicted by the ACE potential trained on LDA data from approximately 810 K to 710 K, closer to the experimental value of 683 K, and substantially widens the stability range of the solid solution. At the same time, the much larger sensitivity to the exchange-correlation functional shows that this level of agreement should not be interpreted as general predictive accuracy. Non-ideal configurational entropy must therefore be sampled explicitly, alongside a careful choice of functional, for a reliable atomistic description of binary phase diagrams.

cond-mat.mtrl-sci↗

arXiv2026

Data-Efficient Training of Linear ACE Potentials through Leverage-Guided Subset Selection of ASSYST Structure Pools

The construction of machine-learned interatomic potentials (MLIPs) is often limited by the cost of generating large density-functional-theory (DFT) training datasets. For systematically generated structure pools such as ASSYST, a central practical question is how many configurations must be labeled to achieve reliable accuracy. Here we assess geometry-based, label-free subset selection for training linear Atomic Cluster Expansion (ACE) potentials. Using statistical leverage scores and CUR-type sampling, we compare leverage-guided selection against random, energy-based, and force-based baselines under controlled iterative protocols. Elemental Al provides the primary benchmark, with Cu and Al-Cu alloys used for transfer validation. Leverage-guided subsets recover plateau-level energy and force accuracy using substantially smaller labeled fractions (approximately 30-40%) than random sampling, corresponding to an effective 2-3x reduction in DFT labeling for the systems studied. In alloy tests, defect energetics remain comparable across strategies once sufficient chemical diversity is included, while leverage selection maintains competitive accuracy at reduced training size. These results demonstrate that descriptor-space-guided, label-free subsampling can significantly reduce DFT workload for linear ACE models trained on ASSYST structure pools without degrading defect-level fidelity.

cond-mat.mtrl-sci↗

arXiv2026

Grain boundary segregation of light elements and their effects on cohesion in ferritic steels

Light elements play an important role in influencing the macroscale properties of engineering alloys through grain boundary (GB) segregation phenomena. However, the scarcity and scattered nature of ab initio datasets for light elements in steels makes reproduction and extraction of general trends from the literature difficult. Here, we present a comprehensive ab initio evaluation of the segregation energies and cohesive effects for H, He, B, C, N, O, P, S, extensively sampling both substitutional and interstitial sites in six model coincident site lattice (CSL) ferritic iron GBs using density functional theory (DFT). Cohesive effects are evaluated in both a quantum-chemistry bond-order and rigid Rice-Wang interfacial cohesive strength framework. Our calculations indicate that, compared at the same concentration, B and C enhance GB cohesion, N, P, H are mildly detrimental, and He, O, S as powerful decohesive agents/embrittlers. Sampling both interstitial and substitutional starting positions is necessary to accurately capture segregation spectra. Commonly utilised sampling criteria such as site volumes prove insufficient for identifying deepest GB binding sites. Solutes placed in either kind of site can induce large relaxations to the same final configuration, resulting in site classification ambiguity. The nearest neighbour distance of a solute to its neighbours after relaxation is shown to be a controlling factor for the lower threshold of segregation energies at sites. The freely available DFT dataset and analysis repositories are expected to advance understanding of GB segregation behaviours of light elements in steels and serve as a resource for developing machine learning interatomic potentials.

cond-mat.mtrl-sci↗

arXiv2026

Microstructural origin of the simultaneous enhancements in strength and ductility of a nitrogen-doped high-entropy alloy

As one of the most abundant interstitial elements, nitrogen (N) is effective in improving yield strength of metallic materials, due to interstitial solid solution strengthening. Doping N can substantially enhance the yield strength but often leads to a decreased ductility, revealing a strength-ductility trade-off phenomenon. Here, we simultaneously enhance the strength and ductility in a non-equiatomic CrMnFeCoNi high-entropy alloy via N alloying and unravel the underlying microscopic mechanisms. The N-doped alloy (1 at.% N) shows an excellent combination of higher yield strength (104% increase) and larger ductility (38% increase), with a two-stage strain hardening behavior, compared to the N-free alloy. Detailed transmission electron microscopy (TEM) analysis reveals that N-doping introduces short-range order (SRO) domains within the microstructure, leads to pronounced planar slip, and promotes the formation of nano-spaced (6-15 nm) stacking faults and deformation twins. Continuous generation and interaction of the fine-spaced SFs act as a strong barrier for dislocation movement and provide ample room for dislocation storage. The interaction of SRO with dislocations and the evolution of SFs ascribe to the first strain hardening stage, and the disordering of the SRO along with the activation of deformation twins are attributed to the second strain hardening stage. Our work shows that N-doping is effective in simultaneously improving the strength-ductility synergy and provides novel insights into alloy design with slightly elevating the SFE, and manipulating the ordered structure within the HEA.

cond-mat.mtrl-sci↗

arXiv2026

Triple Junctions as Dislocation-Like Defects: The Role of Grain Boundary Crystallography Revealed by Experiment and Atomistic Simulation

Grain boundary networks and their evolution are strongly influenced by triple junctions. The defect nature of these line defects significantly affects the properties of the network, but they have not been fully characterized to date. Here, we use scanning transmission electron microscopy combined with atomistic computer simulations to investigate a triple junction at the atomic scale in an Al thin film with {111} texture. Using sampling methods, we were able to construct the same junction structure as in the experiment within a computer model. We present a technique to calculate the Burgers vector of the triple junction. This allows us to connect the junction's dislocation character to the microscopic degrees of freedom of the joining grain boundaries. The junction line energy in the computer model can then be calculated using an embedded atom method potential. It follows the same laws as a bulk dislocation. Finally, we discovered a range of possible triple junctions for the observed grain boundaries, which vary in the magnitude of their Burgers vector. Interestingly, the experimentally observed junction is not the one with the smallest possible Burgers vector and energy. This suggests that the kinetics of transforming the junction line are likely too slow to be driven by the small energy contribution of the triple junction.

cond-mat.mtrl-sci↗

arXiv2025

Domain Boundaries in a Metallic Distortive Polar Metal

Polar metals are an underexplored material class combining two properties that are typically incompatible, namely a polar crystal structure and reasonable electrical conductivity. These intriguing materials offer a unique combination of properties, potentially relevant to optoelectronics, catalysis, memory devices, among other applications. The distortive polar metal (DPM) subclass forms through a symmetry-lifting phase transformation into a non-centrosymmetric polar crystal structure. In the process, domains with uniform geometric polar directions form, oftentimes separated by domain boundaries with polarity discontinuities arranged in "charged" head-to-head (H-H) or tail-to-tail (T-T) morphologies. To date, only metallic oxide DPM microstructures have been studied. Here we reveal, in the intermetallic DPM Mn$_{5}$Al$_{8}$, different surface interactions and electron transfer reactivity at domain boundaries depending on their H-H or T-T character. Variable surface reactivity suggests localized changes in electronic work functions due to an increase (H-H) or decrease (T-T) in the electronic density of states. These findings suggest that metallic DPMs may offer functionalizable domain boundaries and deserve increased attention, given that they allow tunable chemistries and various thermomechanical processing or transformation protocols. Ultimately, this study proposes unconventional metal physics, propelling the discovery and design of advanced electronic materials and devices.

cond-mat.mtrl-sci↗

arXiv2025

User-defined Electrostatic Potentials in DFT Supercell Calculations: Implementation and Application to Electrified Interfaces

Introducing electric fields into density functional theory (DFT) calculations is essential for understanding electrochemical processes, interfacial phenomena, and the behavior of materials under applied bias. However, applying user-defined electrostatic potentials in DFT is nontrivial and often requires direct modification to the specific DFT code. In this work, we present an implementation for supercell DFT calculations under arbitrary electric fields and discuss the required corrections to the energies and forces. The implementation is realized through the recently released VASP-Python interface, enabling the application of user-defined fields directly within the standard VASP software and providing great flexibility and control. We demonstrate the application of this approach with diverse case studies, including molecular adsorption on electrified surfaces, field ion microscopy, electrochemical solid-water interfaces, and implicit solvent models.

cond-mat.mtrl-sci↗

arXiv2025

Computing Solvation Shell Dynamics and Energetics in Electron Transfer Reactions via Molecular Dynamics Simulations

Marcus theory is fundamental to describing electron transfer reactions and quantifying their rates, effectively representing the energy surface associated with an electron transfer from the reactant to the product ionic state via parabolas within a reaction coordinate diagram. Here, we present an intuitive and computationally efficient generalised reaction coordinate amenable to molecular dynamics simulations. By utilising the nuclear charge of the ion, we are able to quantify in a targeted approach changes in the ion's solvation shell, thereby efficiently obtaining the free energy profile associated with the electron transfer, the transition state geometry and the evolution of the water network.

physics.chem-ph↗

arXiv2025

First principles approaches and concepts for electrochemical systems

Ab initio techniques have revolutionised the way in which theory can help practitioners to explore critical mechanisms that govern reactions or properties, and to develop new strategies for materials discovery and design. Yet, their application to electrochemical systems is still limited, due to the challenges electronic structure calculations face in achieving a realistic description of electrified solid/liquid interfaces including, e.g., potential and pH control or free energies of barrier configurations. A well-known example of how novel concepts can extend the scope of simulations is the development of thermostats, which introduced temperature control to electronic structure Density Functional Theory (DFT) calculations. The analogous technique for modelling electrochemical systems - potential control, inherent to most electrochemical experiments - is just emerging. In this review, we critically discuss state-of-the-art approaches to describe electrified interfaces between a solid electrode and a liquid electrolyte in realistic environments. By exchanging energy, electronic charge and ions with their environment, electrochemical interfaces are thermodynamically open systems. In addition, large fluctuations of the electrostatic potential and field occur on the time and length scales relevant to chemical reactions. We systematically discuss the key challenges in incorporating these features into realistic ab initio simulations, as well as the available techniques and approaches to overcome them, in order to facilitate the development and use of these novel techniques by the wider community. These methodological developments provide researchers with a new level of realism to explore fundamental electrochemical mechanisms and reactions from first principles.

cond-mat.mtrl-sci↗

arXiv2025

Macro-Dipole-Constrainted Learning of Atomic Charges for Accurate Electrostatic Potentials at Electrochemical Interfaces

Large thermal fluctuations of the liquid phase obscure the weak macroscopic electric field that drives electrochemical reactions, rendering the extraction of reliable interfacial charge distributions from ab initio molecular dynamics extremely challenging. We introduce SMILE-CP (Scalar Macro-dipole Integrated LEarning - Charge Partitioning), a macro-dipole-constrained scheme that infers atomic charges using only the instantaneous atomic coordinates and the total dipole moment of the simulation cell - quantities routinely available from standard density-functional theory calculations. SMILE-CP preserves both the global electrostatic field and the local potential without invoking any explicit charge-partitioning scheme. Benchmarks on three representative electrochemical interfaces - nanoconfined water, Mg2+ dissolution in water, and a kinked Mg vicinal surface under anodic bias - show that SMILE-CP eliminates the qualitative errors observed for unconstrained charge decompositions. The method is computationally inexpensive and data-efficient, opening the door to charge-aware machine-learning potentials capable of bias-controlled, nanosecond-scale simulations of realistic electrochemical systems.

physics.chem-ph↗

arXiv2025

Ultrastrong and ductile CoNiMoAl medium-entropy alloys enabled by L12 nanoprecipitate-induced multiple deformation mechanisms

L12 precipitates are known to significantly enhance the strength and ductility of single-phase face-centered cubic (FCC) medium- or high-entropy alloys (M/HEAs). However, further improvements in mechanical properties remain untapped, as alloy design has historically focused on systems with specific CrCoNi- or FeCoCrNi-based FCC matrix and Ni3Al L12 phase compositions. This study introduces novel Co-Ni-Mo-Al alloys with L12 precipitates by systematically altering Al content, aiming to bridge this research gap by revealing the strengthening mechanisms. The (CoNi)81Mo12Al7 alloy achieves yield strength of 1086 MPa, tensile strength of 1520 MPa, and ductility of 35 %, demonstrating an impressive synergy of strength, ductility, and strain-hardening capacity. Dislocation analysis via transmission electron microscopy, supported by generalized stacking fault energy (GSFE) calculations using density functional theory (DFT), demonstrates that Mo substitution for Al in the L12 phase alters dislocation behavior, promoting the formation of multiple deformation modes, including stacking faults, super-dislocation pairs, Lomer-Cottrell locks, and unusual nano-twin formation even at low strains. These behaviors are facilitated by the low stacking fault energy (SFE) of the FCC matrix, overlapping of SFs, and dislocation dissociation across anti-phase boundaries (APBs). The increased energy barrier for superlattice intrinsic stacking fault (SISF) formation compared to APBs, due to Mo substitution, further influences dislocation activity. This work demonstrates a novel strategy for designing high-performance M/HEAs by expanding the range of FCC matrix and L12 compositions through precipitation hardening.

cond-mat.mtrl-sci↗

arXiv2025

Temperature and misorientation-dependent austenite nucleation at ferrite grain boundaries in a medium manganese steel: role of misorientation-dependent grain boundary segregation

In the current work, we study the role of grain boundary (GB) misorientation-dependent segregation on austenite nucleation in a 50% cold rolled intercritically annealed 10Mn-0.05C-1.5Al (wt. %) medium Mn steel. During intercritical annealing at 500°C, austenite nucleates predominantly at high-angle GBs. At 600°C, austenite nucleates additionally at low-angle GBs, exhibiting a temperature dependance. Correlative transmission Kikuchi diffraction /atom probe tomography reveals a misorientation-dependent segregation. While GB segregation has been reported to assist austenite nucleation in medium manganese steels (3-12 wt.% Mn), an understanding of the temperature and misorientation dependance is lacking, which is the aim of current work. Since artifacts of atom probe can cause a broadening of the segregation width, we combined experiments with results from density functional theory (DFT) calculations that reveal that the Mn segregation is not limited to the GB plane but confined to a region in the range of approximately 1 nm. Consequently, GB segregation alters both the GB interface energy and the free energy per unit volume corresponding to the transformation. We estimate the local driving force for austenite nucleation accounting for the segregation width of ~ 1 nm. Based on classical nucleation theory, we clarify the effect of GB segregation on the critical radius and activation energy barrier for confined austenite nucleation at the GB.

cond-mat.mtrl-sci↗

arXiv2025

A high-throughput ab initio study of elemental segregation and cohesion at ferritic-iron grain boundaries

Segregation of alloying elements and impurities at grain boundaries (GBs) critically influences material behavior by affecting cohesion. In this study, we present an ab initio high-throughput evaluation of segregation energies and cohesive effects for all elements in the periodic table (Z: 1 to 92, H to U) across six model ferritic iron GBs using density functional theory (DFT). From these data, we construct comprehensive elemental maps for solute segregation tendencies and cohesion at GBs, providing guidance for segregation engineering. We systematically assess the cohesive effects of different elements in all segregating positions along multiple fracture paths with a quantum-chemistry bond-order method as well as a modified Rice-Wang theory of interfacial cohesion. The effects of segregants on the cohesion of GBs are shown to vary drastically as a function of site character, and hence their induced cohesive effects must be considered as a thermodynamic average over the spectral energy distribution. Thus, models that overlook these aspects may fail to accurately predict the impacts of varying alloying concentrations, thermal processing conditions, or GB types. The insights presented here, along with our accompanying dataset, are expected to advance our understanding of GB segregation in steels and other materials.

cond-mat.mtrl-sci↗

arXiv2025

Electric Field-Induced Formation of a 2D Adatom Gas on Cryogenic Li Surfaces

Intense electrostatic fields, such as those able to break bonds and cause field-ion emission, can fundamentally alter the behaviour of atoms at and on the surface. Using density functional theory (DFT) calculations on the Li (110) surface under high electrostatic fields, we identify a critical field strength at which surface atoms occupying a kink site become thermodynamically unstable against adatom formation. This mechanism leads to the formation of a highly concentrated two-dimensional (2D) adatom gas on the surface. Moreover, the applied field reverses the stability of preferred adsorption sites, enabling barrierless diffusion of lithium atoms even well below the threshold required for field evaporation. The here identified mechanisms offer a unified explanation for experimental observations in atom probe tomography and for understanding high electric field phenomena in systems such as battery interfaces and electrochemical environments.

cond-mat.mtrl-sci↗

arXiv2025

Computationally accelerated experimental materials characterization -- drawing inspiration from high-throughput simulation workflows

Computational materials science increasingly benefits from data management, automation, and algorithm-based decision-making for the simulation of material properties and behavior. Experimental materials science also changes rapidly by incorporation of `machine learning' in materials discovery campaigns. The obvious benefits which include automation, reproducibility, data provenance, and reusability of managed data, however, is not widely available in the experimental domain. We present an implementation of a Active Learning loop with a direct interface to an experimental measurement device in pyiron, a framework designed for high-throughput simulations, as demonstrator how to combine experimental and simulated data in one framework. Apart from the acceleration provided by the active learning approach, additional acceleration of the experimental characterization is achieved by using prior knowledge from density functional theory simulations as well as composition-property predictions from literature mining using correlations in word embeddings. With data from all domains in the same framework, a heretofore untapped and much-needed potential for the acceleration of materials characterization and materials discovery campaigns becomes available.

cond-mat.mtrl-sci↗

arXiv2024

SPEA -- an analytical thermodynamic model for defect phase diagram

We propose an analytical thermodynamic model for describing defect phase transformations, which we term the statistical phase evaluation approach (SPEA). The SPEA model assumes a Boltzmann distribution of finite size phase fractions and calculates their statistical average. To benchmark the performance of the model, we apply it to construct binary surface phase diagrams of metal alloys. Two alloy systems are considered: a Mg surface with Ca substitutions and a Ni surface with Nb substitutions. To construct a firm basis against which the performance of the analytical model can be leveled, we first perform Monte Carlo (MC) simulations coupled with cluster expansion of density functional theory dataset. We then demonstrate the SPEA model to reproduce the MC results accurately. Specifically, it correctly predicts the surface order-disorder transitions as well as the coexistence of the 1/3 ordered phase and the disordered phase. Finally, we compare the SPEA method to the sublattice model commonly used in the CALPHAD approach to describe ordered and random solution phases and their transitions. The proposed SPEA model provides a highly efficient approach for modeling defect phase transformations.

cond-mat.mtrl-sci↗