arXiv subjects

Jörg Neugebauer

Publications and source records attributed to Jörg Neugebauer.

At least 37 records · Page 2Linked to original sources

arXiv2024

Dissecting physics of carbon ordering in bcc iron

Zener ordering is a phenomenon that octahedral interstitial atoms such as carbon occupy the same sublattice inside bcc matrix such as iron. The original formulation relies on a mean field theory, which is still most in use today. We employ multiple methods, such as Molecular Dynamics, Metropolis Monte Carlo, Mean Field Theory with chemical interactions and finite temperature effects to show that the Zener ordering for iron carbon systems is governed by local chemical interactions and finite temperature effects and less of mean field nature as described originally by Zener.

cond-mat.mtrl-sci↗

arXiv2024

On the origin of univalent Mg$^+$ ions in solution and their role in anomalous anodic hydrogen evolution

Aqueous metal corrosion is a major economic concern in modern society. A phenomenon that has puzzled generations of scientists in this field is the so-called anomalous hydrogen evolution: the violent dissolution of magnesium under electron-rich (anodic) conditions, accompanied by strong hydrogen evolution, and a key mechanism hampering Mg technology. Experimental studies have indicated the presence of univalent Mg$^+$ in solution, but these findings have been largely ignored because they defy our common chemical understanding and evaded direct experimental observation. Using recent advances in the \emph{ab initio} description of solid-liquid electrochemical interfaces under controlled potential conditions, we described the full reaction path of Mg atom dissolution from a kinked Mg surface under anodic conditions. Our study reveals the formation of a solvated [Mg$^{2+}$(OH)$^-$]$^+$ ion complex, challenging the conventional assumption of Mg$^{2+}$ ion. This insight provides an intuitive explanation for the postulated presence of (coulombically) univalent Mg$^+$ ions and the absence of protective oxide/hydroxide layers normally formed under anodic/oxidizing conditions. The discovery of this unexpected and unconventional reaction mechanism is crucial for identifying new strategies for corrosion prevention and can be transferred to other metals.

cond-mat.mtrl-sci↗

arXiv2024

Development of an Atomic Cluster Expansion potential for iron and its oxides

The combined structural and electronic complexity of iron oxides poses many challenges to atomistic modeling. To leverage limitations in terms of the accessible length and time scales, one requires a physically justified interatomic potential which is accurate to correctly account for the complexity of iron-oxygen systems. Such a potential is not yet available in the literature. In this work, we propose a machine-learning potential based on the Atomic Cluster Expansion for modeling the iron-oxygen system, which explicitly accounts for magnetism. We test the potential on a wide range of properties of iron and its oxides, and demonstrate its ability to describe the thermodynamics of systems spanning the whole range of oxygen content and including magnetic degrees of freedom.

cond-mat.mtrl-sci↗

arXiv2024

Roadmap on Data-Centric Materials Science

Science is and always has been based on data, but the terms "data-centric" and the "4th paradigm of" materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of Artificial Intelligence (AI) and its subset Machine Learning (ML), has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research.

cond-mat.mtrl-sci↗

arXiv2024

From electrons to phase diagrams with classical and machine learning potentials: automated workflows for materials science with pyiron

We present a comprehensive and user-friendly framework built upon the pyiron integrated development environment (IDE), enabling researchers to perform the entire Machine Learning Potential (MLP) development cycle consisting of (i) creating systematic DFT databases, (ii) fitting the Density Functional Theory (DFT) data to empirical potentials or MLPs, and (iii) validating the potentials in a largely automatic approach. The power and performance of this framework are demonstrated for three conceptually very different classes of interatomic potentials: an empirical potential (embedded atom method - EAM), neural networks (high-dimensional neural network potentials - HDNNP) and expansions in basis sets (atomic cluster expansion - ACE). As an advanced example for validation and application, we show the computation of a binary composition-temperature phase diagram for Al-Li, a technologically important lightweight alloy system with applications in the aerospace industry.

cond-mat.mtrl-sci↗

arXiv2024

Understanding atom probe's analytical performance for iron oxides using correlation histograms and ab initio calculations

Field evaporation from ionic or covalently bonded materials often leads to the emission of molecular ions. The metastability of these molecular ions, particularly under the influence of the intense electrostatic field (1010 Vm-1), makes them prone to dissociation with or without an exchange of energy amongst them. These processes can affect the analytical performance of atom probe tomography (APT). For instance, neutral species formed through dissociation may not be detected at all or with a time of flight no longer related to their mass, causing their loss from the analysis. Here, we evaluated the changes in the measured composition of FeO, Fe2O3 and Fe3O4 across a wide range of analysis conditions. Possible dissociation reactions are predicted by density-functional theory (DFT) calculations considering the spin states of the molecules. The energetically favoured reactions are traced on to the multi-hit ion correlation histograms, to confirm their existence within experiments, using an automated Python-based routine. The detected reactions are carefully analysed to reflect upon the influence of these neutrals from dissociation reactions on the performance of APT for analysing iron oxides.

cond-mat.mtrl-sci↗

arXiv2023

Quantification of electronic and magnetoelastic mechanisms of first-order magnetic phase transitions from first principles: application to caloric effects in La(Fe$_x$Si$_{1-x}$)$_{13}$

La(Fe$_x$Si$_{1-x}$)$_{13}$ and derived quaternary compounds are well-known for their giant, tunable, magneto- and barocaloric responses around a first-order paramagnetic-ferromagnetic transition near room temperature with low hysteresis. Remarkably, such a transition shows a large spontaneous volume change together with itinerant electron metamagnetic features. While magnetovolume effects are well-established mechanisms driving first-order transitions, purely electronic sources have a long, subtle history and remain poorly understood. Here we apply a disordered local moment picture to quantify electronic and magnetoelastic effects at finite temperature in La(Fe$_x$Si$_{1-x}$)$_{13}$ from first-principles. We obtain results in very good agreement with experiment and demonstrate that the magnetoelastic coupling, rather than purely electronic mechanisms, drives the first-order character and causes at the same time a huge electronic entropy contribution to the caloric response.

cond-mat.mtrl-sci↗

arXiv2023

Constructing phase diagrams for defects by correlated atomic-scale characterization

Phase transformations and crystallographic defects are two essential tools to drive innovations in materials. Bulk materials design via tuning chemical compositions has been systematized using phase diagrams. We show here that the same thermodynamic concept can be applied to understand the chemistry at defects. We present a combined experimental and modelling approach to scope and build phase diagrams for defects. The discovery was enabled by triggering phase transformations of individual defects through local alloying, and sequentially imaging the structural and chemical changes using atomic-resolution scanning transmission electron microscopy. By observing atomic-scale phase transformations of a Mg grain boundary through Ga alloying, we exemplified the method to construct a grain boundary phase diagram using ab initio simulations and thermodynamic principles. The methodology enables a systematic development of defect phase diagrams to propel a new paradigm for materials design utilizing chemical complexity and phase transformations at defects.

cond-mat.mtrl-sci↗

arXiv2023

Construction and analysis of surface phase diagrams to describe segregation and dissolution behavior of Al and Ca in Mg alloys

Segregation and dissolution behavior of Mg alloyed with Ca and Al are studied by performing density functional theory calculations considering an extensive set of surface structures and compositions. Combining ab initio surface science approaches with cluster expansion for ordered surface structures we construct surface phase diagrams for these alloys. We utilize these diagrams to study segregation phenomena and chemical trends for surfaces in contact with a dry environment or with an aqueous electrolyte. We show that the presence of water dramatically impacts the stability and chemical composition of the considered metallic surfaces. We furthermore find that the two alloying elements behave qualitatively different: whereas Ca strongly segregates to the surface and becomes dissolved upon exposure of the surface to water, Al shows an anti-segregation behavior, i.e., it remains in Mg bulk. These findings provide an explanation for the experimentally observed increase/decrease in corrosion rates when alloying Mg with Al/Ca.

cond-mat.mtrl-sci↗

arXiv2023

Accurate computation of chemical contrast in field ion microscopy

We present a computational approach to simulate local contrast observed in Field Ion Microscopy (FIM). It is based on density-functional theory utilizing the Tersoff-Hamann approach as done in Scanning Tunneling Microscopy (STM). A key requirement is the highly accurate computation of the surface states' wave-function tails. To refine the Kohn-Sham states from standard iterative global solvers we introduce and discuss the EXtrapolation of Tails via Reverse integration Algorithm (EXTRA). The decaying tails are obtained by reverse integration (from outside in) using a Numerov-like algorithm. The starting conditions are then iteratively adapted to match the values of plane-wave Kohn-Sham wave functions close to the surface. We demonstrate the performance of the proposed algorithm by analysing and showing the chemical contrast for Ta at Ni surface.

cond-mat.mtrl-sci↗

arXiv2023

A machine learning framework for quantifying chemical segregation and microstructural features in atom probe tomography data

Atom probe tomography (APT) is ideally suited to characterize and understand the interplay of chemical segregation and microstructure in modern multicomponent materials. Yet, the quantitative analysis typically relies on human expertise to define regions of interest. We introduce a computationally efficient, multistage machine learning strategy to identify chemically distinct domains in a semi automated way, and subsequently quantify their geometric and compositional characteristics. In our algorithmic pipeline, we first coarse grain the APT data into voxels, collect the composition statistics, and decompose it via clustering in composition space. The composition classification then enables the real space segmentation via a density based clustering algorithm, thus revealing the microstructure at voxel resolution. Our approach is demonstrated for a Sm(Co,Fe)ZrCu alloy. The alloy exhibits two precipitate phases with a plate-like, but intertwined morphology. The primary segmentation is further refined to disentangle these geometrically complex precipitates into individual plate like parts by an unsupervised approach based on principle component analysis, or a U-Net based semantic segmentation trained on the former. Following the chemical and geometric analysis, detailed chemical distribution and segregation effects relative to the predominant plate-like geometry can be readily mapped without resorting to the initial voxelization.

physics.chem-ph↗

arXiv2023

Machine learning-enabled tomographic imaging of chemical short-range atomic ordering

In solids, chemical short-range order (CSRO) refers to the self-organisation of atoms of certain species occupying specific crystal sites. CSRO is increasingly being envisaged as a lever to tailor the mechanical and functional properties of materials. Yet quantitative relationships between properties and the morphology, number density, and atomic configurations of CSRO domains remain elusive. Herein, we showcase how machine learning-enhanced atom probe tomography (APT) can mine the near-atomically resolved APT data and jointly exploit the technique's high elemental sensitivity to provide a 3D quantitative analysis of CSRO in a CoCrNi medium-entropy alloy. We reveal multiple CSRO configurations, with their formation supported by state-of-the-art Monte-Carlo simulations. Quantitative analysis of these CSROs allows us to establish relationships between processing parameters and physical properties. The unambiguous characterization of CSRO will help refine strategies for designing advanced materials by manipulating atomic-scale architectures.

cond-mat.mtrl-sci↗

arXiv2023

Systematic Atomic Structure Datasets for Machine Learning Potentials: Application to Defects in Magnesium

We present a physically motivated strategy for the construction of training sets for transferable machine learning interatomic potentials. It is based on a systematic exploration of all possible space groups in random crystal structures, together with deformations of cell shape, size, and atomic positions. The resulting potentials turn out to be unbiased and generically applicable to studies of bulk defects without including any defect structures in the training set or employing any additional Active Learning. Using this approach we construct transferable potentials for pure Magnesium that reproduce the properties of hexagonal closed packed (hcp) and body centered cubic (bcc) polymorphs very well. In the process we investigate how different types of training structures impact the properties and the predictive power of the resulting potential.

cond-mat.mtrl-sci↗

arXiv2022

Ab initio vacancy formation energies and kinetics at metal surfaces under high electric field

Recording field ion microscope images under field evaporating conditions and subsequently reconstructing the underlying atomic configuration, called three-dimensional field ion microscopy (3D-FIM) is one of the few techniques capable of resolving crystalline defects at an atomic scale. However, the quantification of the observed vacancies and their origins are still a matter of debate. It was suggested that high electric fields (1-5 V/Å) used in 3D-FIM could introduce artefact vacancies. To investigate such effects, we used density functional theory (DFT) simulations. Stepped Ni and Pt surfaces with kinks were modelled in the repeated slab approach with a (971) surface orientation. An electrostatic field of up to 4 V/Å was introduced on one side of the slab using the generalized dipole correction. Contrary to what was proposed, we show that the formation of vacancies on the electrified metal surface is more difficult compared to a field-free case. We also find that the electric field can introduce kinetic barriers to a potential vacancy-annihilation mechanism. We rationalize these findings by comparing to insights from field evaporation models.

cond-mat.mtrl-sci↗

arXiv2022

Machine learning-enabled high-entropy alloy discovery

High-entropy alloys are solid solutions of multiple principal elements, capable of reaching composition and feature regimes inaccessible for dilute materials. Discovering those with valuable properties, however, relies on serendipity, as thermodynamic alloy design rules alone often fail in high-dimensional composition spaces. Here, we propose an active-learning strategy to accelerate the design of novel high-entropy Invar alloys in a practically infinite compositional space, based on very sparse data. Our approach works as a closed-loop, integrating machine learning with density-functional theory, thermodynamic calculations, and experiments. After processing and characterizing 17 new alloys (out of millions of possible compositions), we identified 2 high-entropy Invar alloys with extremely low thermal expansion coefficients around 2*10-6 K-1 at 300 K. Our study thus opens a new pathway for the fast and automated discovery of high-entropy alloys with optimal thermal, magnetic and electrical properties.

cond-mat.mtrl-sci↗

arXiv2022

Ab initio calculation of the magnetic Gibbs free energy of materials using magnetically constrained supercells

We present a first-principles approach for the computation of the magnetic Gibbs free energy of materials using magnetically constrained supercell calculations. Our approach is based on an adiabatic approximation of slowly varying local moment orientations, the so-called finite-temperature disordered local moment picture. It describes magnetic phase transitions and how electronic and/or magnetostructural mechanisms generate a discontinuous (first-order) character. We demonstrate that the statistical mechanics of the local moment orientations can be described by an affordable number of supercell calculations containing noncollinear magnetic configurations. The applicability of our approach is illustrated by firstly studying the ferromagnetic state in bcc Fe. We then investigate the temperature-dependent properties of a triangular antiferromagnetic state stabilizing in two antiperovskite systems Mn$_3$AN (A = Ga, Ni). Our calculations provide the negative volume expansion of these materials as well as the ab initio origin of the discontinuous character of the phase transitions, electronic and/or magnetostructural, in good agreement with experiment.

cond-mat.mtrl-sci↗

arXiv2021

Automated optimization of convergence parameters in plane wave density functional theory calculations via a tensor decomposition-based uncertainty quantification

First principles approaches have revolutionized our ability in using computers to predict, explore and design materials. A major advantage commonly associated with these approaches is that they are fully parameter free. However, numerically solving the underlying equations requires to choose a set of convergence parameters. With the advent of high-throughput calculations it becomes exceedingly important to achieve a truly parameter free approach. Utilizing uncertainty quantification (UQ) and tensor decomposition we derive a numerically highly efficient representation of the statistical and systematic error in the multidimensional space of the convergence parameters. Based on this formalism we implement a fully automated approach that requires as input the target accuracy rather than convergence parameters. The performance and robustness of the approach are shown by applying it to a large set of elements crystallizing in a cubic fcc lattice.

cond-mat.mtrl-sci↗

arXiv2021

Understanding alkali contamination in colloidal nanomaterials to unlock grain boundary impurity engineering

Metal nano-aerogels combine a large surface area, a high structural stability, and a high catalytic activity towards a variety of chemical reactions. The performance of such nanostructures is underpinned by the atomic-level distribution of their constituents. Yet monitoring their sub-nanoscale structure and composition to guide property optimization remains extremely challenging. Here, we synthesized Pd nano-aerogels from a K2PdCl4 precursor and two different NaBH4 reductant concentrations in distilled water. Atom probe tomography reveals that the aerogel is poly-crystalline and that impurities (Na, K) are integrated from the solution into grain boundaries. Ab initio calculations indicate that these impurities preferentially bound to the Pd-metal surface and are ultimately found in grain boundaries forming as the particles coalesce during synthesis, with Na atoms thermodynamically equilibrating with the surrounding solution and K atoms remaining between growing grains. If controlled, impurity integration, i.e. grain boundary decoration, may offer opportunities for designing new nano-aerogels.

cond-mat.mtrl-sci↗