arXiv subjects

Sandra Korte-Kerzel

Publications and source records attributed to Sandra Korte-Kerzel.

At least 19 recordsLinked to original sources

arXiv2026

Zonal dislocations in Laves phases: A coupled synchro-shear slip mechanism

Synchro-shear is the primary plastic deformation mechanism in Laves phases at elevated temperatures, mediated by synchro-Shockley partial dislocations-zonal dislocations that proceed via localized events such as kink-pair nucleation and propagation. Using atomistic simulations, we identified a novel slip mechanism in Laves phases, namely coupled synchro-shear slip, involving the synchronized glide of two synchro-Shockley partial dislocations on adjacent slip planes, leading to the formation of extrinsic stacking faults. High-resolution scanning transmission electron microscopy revealed the extended core structures consistent with coupled synchro-Shockley partial dislocations bounded by extrinsic stacking faults in C15 NbCr2 and their involvement in twinning. These results highlight the critical role of coupled synchro-shear slip in enabling phase transformations between Laves polytypes and in governing twinning behavior, providing new atomistic insight into the kinetic nature of plasticity in topologically close-packed intermetallic phases.

cond-mat.mtrl-sci↗

arXiv2026

Composition- and Ordering-Dependent Evolution of Simulated Kikuchi Patterns of Au-Ni Alloys

Understanding how Kikuchi patterns behave with subtle material variations is essential for developing machine learning (ML) based indexing methods for structurally and chemically complex cases, such as phases with potential sub-lattice order or (meta)stable defects with segregation (defect phases). Simulated Kikuchi patterns of the binary Au-Ni system were systematically analysed to investigate the effects of lattice parameter, chemical composition, partial site occupancy and ordering. The full compositional range from pure nickel to pure gold was considered, including a hypothetical ordered L$1_2$ Au$_3$Ni structure. Simulation parameters were optimised by comparison with experimental patterns. Normalized cross correlation showed limited sensibility to subtle differences between patterns. EMsoft simulation results revealed a systematic increase in mean intensity and more reflections contribute significantly as gold content increases. A surprising four-fold increase in mean intensity from 99 % gold to the pure gold sample highlighted limitations of partial site occupancy simulation by EMsoft. Difference maps showed enhanced normalised intensity along the {111} and {200} bands for the sample with 20 % gold compared to higher gold compositions. By isolating lattice parameter and chemical composition effects, chemical composition contributes predominantly to the mean intensity and strong reflections count, although the normalised intensity distribution was affected by both. Introducing L$1_2$ ordering increased the number of strong reflections and mean intensity, and redistributed normalised intensity along {111} and {200} bands and selected zone axis. Normalised intensity distribution and band width, are key descriptions to distinguish the Kikuchi patterns, which can be incorporated in future representation learning based indexing methods.

cond-mat.mtrl-sci↗

arXiv2026

Stochastic twinning in confined volumes of Mg: Insights from in-situ micromechanical testing and atomistic simulations

Tensile twinning plays a central role in accommodating -axis plasticity in Mg. In bulk Mg, twinning typically shows a relatively deterministic response with a low critical stress, whereas in confined volumes it exhibits pronounced scatter, complicating the prediction of small-scale mechanical behavior. In this study, we investigate the origin of this stochasticity by combining site-specific micropillar compression with atomistic simulations. Experiments show that under -axis compression, plastic deformation is dominated by {10-12} twinning, with each discrete stress drop in the stress-strain response marking the activation and rapid advance of a twin. Atomistic simulations further separate twinning into two mechanistic regimes: nucleation and longitudinal propagation occur in a high-stress, shuffle-assisted regime, whereas lateral thickening proceeds in a low-stress regime controlled by disconnection glide. Linking these mechanistic insights with post-mortem characterization of deformed pillars demonstrates that the scatter in measured yield stresses arises from stochastic selection among competing twinning pathways, governed by the local defect landscape (presence, distribution, and morphology of pre-existing defects). Overall, this work identifies an atomistic basis for size-dependent stochastic twinning in Mg and provides a general framework for materials whose plasticity is controlled by discrete activation events.

cond-mat.mtrl-sci↗

arXiv2026

EBSD and Subtle Crystallographic Differences - A Study of Resolving Interlayer Spacings in Nb-Ni and Nb-Co mu-phases

In ordered intermetallics, slight variations in lattice site occupancy and specific interlayer spacings have been identified as the sources of significant changes in critical resolved shear stress and therefore how a given phase may affect alloy properties. So far, atom positions and lattice site occupancies have traditionally been characterised by high-resolution transmission electron microscopy (HR-TEM) and X-ray diffraction (XRD), which are methods that offer either local detail or high statistical significance but not both. Electron backscatter diffraction (EBSD), by contrast, provides high spatial resolution across large sample areas and therefore, has the potential to enable the local investigation of interlayer spacing and site lattice occupancy with improved statistical reliability. The objectives of the study are to benchmark EBSDs capability for resolving these subtle features and to correlate them with compositional and mechanical properties. In this case study, we therefore show that EBSD can resolve key crystallographic features of mu-phase intermetallics, specifically interlayer spacings. We combine pattern matching with large-scale dynamical simulations of template libraries guided by XRD based information on lattice parameters. For this, we generate structures that vary in the spacing between triple-layer and Kagome layer and in the site lattice occupancy of the 3a site. This approach successfully predicts the change of interlayer spacing between Kagome and triple layers in Nb-Co and Nb-Ni mu-phases, in good agreement with XRD and HR-TEM.

cond-mat.mtrl-sci↗

arXiv2026

SPARSE -- Efficient High-Resolution SEM Imaging of Rare Microstructural Features Across Large Areas by Selective Rescanning

Characterisation of rare microstructural features in scanning electron microscopy (SEM) requires imaging large areas at high resolution. This leads to prohibitively long acquisition times. We present an open-source Python framework that addresses this bottleneck through a two-stage approach: a fast scan identifies regions of interest, which are then selectively rescanned with imaging parameters suitable for quantitative analysis. The framework defines a generic microscope interface and a modular detection interface, allowing adaptation to different microscope platforms and detection methods. Scanning, detection, and rescanning are parallelized using separate processes, ensuring that computation time does not extend acquisition time. The two processes communicate exclusively through queues, avoiding shared mutable state and eliminating the need for explicit synchronization. We validate the framework on damage detection in dual-phase DP800 steel using a Tescan Clara SEM. For a representative configuration a detection rate of 99 % is achieved at approximately 58 % of the conventional acquisition time. At 95 % detection rate, acquisition time drops to 19 %. These time savings estimates represent lower bounds based on the ratio of scanned pixels. The complete implementation will be made available upon publication and upon request during peer-review.

physics.app-ph↗

arXiv2025

AstroECP: towards more practical Electron Channeling Contrast Imaging

Electron channeling contrast imaging (ECCI) is a scanning electron microscopy (SEM) based technique that enables bulk-sample characterization of crystallographic defects (e.g. dislocations, stacking faults, low angle boundaries). Despite its potential, ECCI remains underused for quantitative defect analysis as compared to transmission electron microscope (TEM) based methods. Here, we overcome barriers that limit the use of ECCI including optimizing signal-to-noise contrast, precise determination of the incident beam vector with calibrated and easy to use simulations and experimental selected area electron channeling patterns (SA-ECP). We introduce a systematic ECCI workflow, alongside a new open-source software tool (AstroECP), that includes calibration of stage tilting, SA-ECP field of view, and the energy that forms the ECP/ECCI contrast using dynamical simulations. The functionality of this workflow is demonstrated with case studies that include threading dislocations in GaAs and the cross validation of precession based ECCI-contrast, which is otherwise known as Electron Channeling Orientation Determination (eCHORD). To assist the reader, we also provide best practice guidelines for ECCI implementation to promote high-resolution defect imaging in the SEM.

cond-mat.mtrl-sci↗

arXiv2025

Towards Defect Phase Diagrams: From Research Data Management to Automated Workflows

Defect phase diagrams provide a unified description of crystal defect states for materials design and are central to the scientific objectives of the Collaborative Research Centre (CRC) 1394. Their construction requires the systematic integration of heterogeneous experimental and simulation data across research groups and locations. In this setting, research data management (RDM) is a key enabler of new scientific insight by linking distributed research activities and making complex data reproducible and reusable. To address the challenge of heterogeneous data sources and formats, a comprehensive RDM infrastructure has been established that links experiment, data, and analysis in a seamless workflow. The system combines: (1) a joint electronic laboratory notebook and laboratory information management system, (2) easy-to-use large-object data storage, (3) automatic metadata extraction from heterogeneous and proprietary file formats, (4) interactive provenance graphs for data exploration and reuse, and (5) automated reporting and analysis workflows. The two key technological elements are the openBIS electronic laboratory notebook and laboratory information management system, and a newly developed companion application that extends openBIS with large-scale data handling, automated metadata capture, and federated access to distributed research data. This integrated approach reduces friction in data capture and curation, enabling traceable and reusable datasets that accelerate the construction of defect phase diagrams across institutions.

cs.DB↗

arXiv2025

Chemically tailored planar defect phases in the Ta-Fe μ-phase

Intermetallics often exhibit complex crystal structures, which give rise to intricate defect structures that critically influence their mechanical and functional properties. Despite studies on individual defect types, a comprehensive understanding of the defect landscape in μ-phases, a class of topologically close-packed phases, remains elusive. In this study, we investigated the planar defect structures in the Ta-Fe μ-phase across a compositional range of 46 to 58 at.% Ta using electron microscopy and density functional theory calculations. Electron backscatter diffraction and high-resolution scanning transmission electron microscopy reveal a transition from basal twin boundaries and planar faults containing C14 TaFe2 Laves phase layers at a low Ta content to pyramidal {1\bar{1}02} twins at a higher Ta content. Density functional theory calculations of defect formation energies confirm a chemical potential-driven stabilisation of Laves phase lamellae. The prevalence of pyramidal twins in Ta-rich μ-phase samples is attributed to the competitive nature of different planar defects during solidification. A defect landscape for μ-phases is proposed, illustrating the interplay between site occupancy, dislocation types and planar faults across the chemical potential space. These findings provide fundamental insights into defect engineering in structurally complex intermetallics and open pathways for optimising material properties through chemical tuning.

cond-mat.mtrl-sci↗

arXiv2025

Solute Co-Segregation Mechanisms at Low-Angle Grain Boundaries in Magnesium: A Combined Atomic-Scale Experimental and Modeling Study

Solute segregation at low-angle grain boundaries (LAGBs) critically affects the microstructure and mechanical properties of magnesium (Mg) alloys. In modern alloys containing multiple substitutional elements, understanding solute-solute interactions at microstructural defects becomes essential for alloy design. This study investigates the co-segregation mechanisms of calcium (Ca), zinc (Zn), and aluminum (Al) at a LAGB in a dilute AZX010 Mg alloy by combining atomic-scale experimental and modeling techniques. Three-dimensional atom probe tomography (3D-APT) revealed significant segregation of Ca, Zn, and Al at the LAGB, with Ca forming linear segregation patterns along dislocation arrays characteristic of the LAGB. Clustering analysis showed increased Ca-Ca pairs at the boundary, indicating synergistic solute interactions. Atomistic simulations and elastic dipole calculations demonstrated that larger Ca atoms prefer tensile regions around dislocations, while smaller Zn and Al atoms favor compressive areas. These simulations also found that Ca-Ca co-segregation near dislocation cores is energetically more favorable than other solute pairings, explaining the enhanced Ca clustering observed experimentally. Thermodynamic modeling incorporating calculated segregation energies and solute-solute interactions accurately predicted solute concentrations at the LAGB, aligning with experimental data. The findings emphasize the importance of solute interactions at dislocation cores in Mg alloys, offering insights for improving mechanical performance through targeted alloying and grain boundary engineering.

cond-mat.mtrl-sci↗

arXiv2025

Predicting Grain Boundary Segregation in Magnesium Alloys: An Atomistically Informed Machine Learning Approach

Grain boundary (GB) segregation in magnesium (Mg) substantially influences its mechanical properties and performance. Atomic-scale modelling, typically using ab-initio or semi-empirical approaches, has mainly focused on GB segregation at highly symmetric GBs in Mg alloys, often failing to capture the diversity of local atomic environments and segregation energies, resulting in inaccurate structure-property predictions. This study employs atomistic simulations and machine learning models to systematically investigate the segregation behavior of common solute elements in polycrystalline Mg at both 0 K and finite temperatures. The machine learning models accurately predict segregation thermodynamics by incorporating energetic and structural descriptors. We found that segregation energy and vibrational free energy follow skew-normal distributions, with hydrostatic stress, an indicator of excess free volume, emerging as an important factor influencing segregation tendency. The local atomic environment's flexibility, quantified by flexibility volume, is also crucial in predicting GB segregation. Comparing the grain boundary solute concentrations calculated via the Langmuir-McLean isotherm with experimental data, we identified a pronounced segregation tendency for Nd, highlighting its potential for GB engineering in Mg alloys. This work demonstrates the powerful synergy of atomistic simulations and machine learning, paving the way for designing advanced lightweight Mg alloys with tailored properties.

cond-mat.mtrl-sci↗

arXiv2025

First-Principles Insights into the Site Occupancy of Ta-Fe-Al C14 Laves Phases

This study investigates the site occupancy preferences of Al in Ta(Fe$_{1-x}$Al$_x$)$_2$ Laves phases using first-principles calculations, covering Al concentrations from 0 to 50 at.\%. Al atoms exhibit a strong preference for $2a$ Wyckoff sites, with configurations becoming more energetically favorable as these sites reach full occupancy at high Al concentrations. Magnetic configurations were explored, revealing that anti-ferromagnetic ordering is the most favorable at ground states. A metastable defect phase diagram based on the chemical potential of Al was constructed to map site occupancy preferences, where Ta$_4$Fe$_6$Al$_2$ and Ta$_4$Fe$_2$Al$_6$ exhibit the widest chemical potential windows. The correlation between lattice distortions and site occupancy was examined, demonstrating that symmetric Al distributions enhance structural preference. These findings offer insights into the structural motifs of the Ta-Fe-Al system, providing a foundation for future investigations on structure-property relationships.

cond-mat.mtrl-sci↗

arXiv2024

Resolution Enhancement of Scanning Electron Micrographs using Artificial Intelligence

Scanning Electron Microscopy (SEM) is pivotal in revealing intricate micro- and nanoscale features across various research fields. However, obtaining high-resolution SEM images presents challenges, including prolonged scanning durations and potential sample degradation due to extended electron beam exposure. This paper addresses these challenges by training and applying a deep learning based super-resolution algorithm. We show that the chosen algorithm is capable of increasing the resolution by a factor of 4, thereby reducing the initial imaging time by a factor of 16. We benchmark our method in terms of visual similarity and similarity metrics on two different materials, a dual-phase steel and a case-hardening steel, improving over standard interpolation methods. Additionally, we introduce an experimental pipeline for the study of rare events in scanning electron micrographs, without losing high-resolution information.

eess.IV↗

arXiv2024

Mechanical properties and deformation mechanisms of the C14 Laves and μ-phase in the ternary Ta-Fe(-Al) system

As structural and functional materials, topologically close-packed (TCP) phases of transition metal compounds offer a wide range of attractive properties. Due to their complex crystal structure and the resulting brittleness, the knowledge on their mechanical behaviour is still very limited, especially below the brittle-to-ductile transition temperature. In this study, we systematically analyse the influence of composition and crystal structure on the mechanical properties and deformation mechanisms in the binary Ta-Fe system as well as in the ternary Ta-Fe-Al system as both systems contain a hexagonal C14 Laves and a mu-phase. We use nanoindentation, slip trace analysis and transmission electron microscopy to study the influence of crystal structure, composition and crystal orientation. The composition strongly influences the indentation modulus in the binary Ta-Fe system, showing a decreasing trend with increasing Ta content. The addition of Al, however, does not lead to a significant change of the mechanical properties of the ternary TCP phases. The investigation of the deformation mechanisms revealed that the Laves phase primarily deforms via non-basal slip while the basal plane is the favoured slip plane in the mu-phase. By partly replacing Fe with Al, the plasticity is not affected strongly, but the proportion of non-basal slip slightly increases for both ternary TCP phases compared to the binary ones.

cond-mat.mtrl-sci↗

arXiv2024

Beyond Fundamental Building Blocks: Plasticity in Structurally Complex Crystals

Intermetallics, which encompass a wide range of compounds, often exhibit similar or closely related crystal structures, resulting in various intermetallic systems with structurally derivative phases. This study examines the hypothesis that deformation behavior can be transferred from fundamental building blocks to structurally related phases using the binary samarium-cobalt system. We investigate SmCo$_2$ and SmCo$_5$ as fundamental building blocks and compare them to the structurally related SmCo$_3$ and Sm$_2$Co$_{17}$ phases. Nanoindentation and micropillar compression tests were performed to characterize the primary slip systems, complemented by generalized stacking fault energy calculations via atomic-scale modeling. Our results show that while elastic properties of the structurally complex phases follow a rule of mixtures, their plastic deformation mechanisms are more intricate, influenced by the stacking and bonding nature within the crystal's building blocks. These findings underscore the importance of local bonding environments in predicting the mechanical behavior of structurally related intermetallics, providing crucial insights for the development of high-performance intermetallic materials.

cond-mat.mtrl-sci↗

arXiv2024

Grain boundary segregation spectrum in basal-textured Mg alloys: From solute decoration to structural transition

Mg alloys are promising lightweight structural materials due to their low density and excellent mechanical properties. However, their limited formability and ductility necessitate improvements in these properties, specifically through texture modification via grain boundary segregation. While significant efforts have been made, the segregation behavior in Mg polycrystals, particularly with basal texture, remains largely unexplored. In this study, we performed atomistic simulations to investigate grain boundary segregation in dilute and concentrated solid solution Mg-Al alloys. We computed the segregation energy spectrum of basal-textured Mg polycrystals, highlighting the contribution from specific grain boundary sites, such as junctions, and identified a newly discovered bimodal distribution which is distinct compared to the conventional skew-normal distribution found in randomly-oriented polycrystals. Using a hybrid molecular dynamics/Monte Carlo approach, we simulated segregation behavior at finite temperatures, identifying grain boundary structural transitions, particularly the varied fraction and morphology of topologically close-packed grain boundary phases when changing thermodynamic variables. The outcomes of this study offer crucial insights into basal-textured grain boundary segregation and phase formation, which can be extended to other relevant Mg alloys containing topologically close-packed intermetallics.

cond-mat.mtrl-sci↗

arXiv2024

Influence of chemical composition on the room temperature plas-ticity of C15 Ca-Al-Mg Laves phases

The influence of chemical composition changes on the room temperature mechanical proper-ties in the C15 CaAl2 Laves phase were investigated in two off-stoichiometric compositions with 5.7 at.-% Mg addition (Ca33Al61Mg6) and 10.8 at.-% Mg and 3.0 at.-% Ca addition (Ca36Al53Mg11) and compared to the stoichiometric (Ca33Al67) composition. Cubic Ca-Al-Mg Laves phases with multiple crystallographic orientations were characterised and deformed using nanoindentation. The hardness and indentation modulus were measured to be 4.1 +- 0.3 GPa and 71.3 +- 1.5 GPa for Ca36Al53Mg11, 4.6 +- 0.2 GPa and 80.4 +- 3.8 GPa for Ca33Al61Mg6 and 4.9 +- 0.3 GPa and 85.5 +- 4.0 GPa for Ca33Al67, respectively. The resulting surface traces as well as slip and crack planes, were distinguished on the indentation surfac-es, revealing the activation of several different {11n} slip systems, as further confirmed by conventional transmission electron microscopic observations. Additionally, the deformation mechanisms and corresponding energy barriers of activated slip systems were evaluated by atomistic simulations.

cond-mat.mtrl-sci↗

arXiv2024

Nanoscale brittle-to-ductile transition of the C15 CaAl$_2$ Laves phase

The influence of temperature on the deformation behaviour of the C15 CaAl$_2$ Laves phase, a key constituent for enhancing the mechanical properties of Mg alloys up to service temperatures of 200 °C, remains largely unexplored. This study presents, for the first time, the nanoscale brittle-to-ductile transition (BDT) of this intermetallic phase through in situ testing including nanoindentation, scratch testing, and micropillar splitting conducted at elevated temperatures. By correlating observations from these techniques, changes in deformation of CaAl$_2$ were identified in relation to temperature. High-temperature nanoindentation quantitatively determined the temperature range for the BDT, and revealed that CaAl$_2$ undergoes a BDT at ~0.55T$_m$, exhibiting an intermediate region of microplasticity. A noticeable decrease in nanoindentation hardness was observed at ~450-500 °C, accompanied by an increase in residual indent size, while indentation cracking was not observed above 300 °C. Results from high-temperature micropillar splitting revealed cracking and brittle pillar splitting up to 300 °C, with an increase in apparent fracture toughness from 0.9 $\pm$ 0.1 MPa$\cdot\sqrt m$ to 2.8 $\pm$ 0.3 MPa$\cdot\sqrt m$, and subsequent crack-free plastic deformation from 400 °C. Transmission electron microscopy analysis of the deformed material from nanoindentation revealed that the BDT of CaAl$_2$ may be attributed to enhanced dislocation plasticity with increasing temperature.

cond-mat.mtrl-sci↗

arXiv2024

Automated Segmentation of Large Image Datasets using Artificial Intelligence for Microstructure Characterisation, Damage Analysis and High-Throughput Modelling Input

Many properties of commonly used materials are driven by their microstructure, which can be influenced by the composition and manufacturing processes. To optimise future materials, understanding the microstructure is critically important. Here, we present two novel approaches based on artificial intelligence that allow the segmentation of the phases of a microstructure for which simple numerical approaches, such as thresholding, are not applicable: One is based on the nnU-Net neural network, and the other on generative adversarial networks (GAN). Using large panoramic scanning electron microscopy images of dual-phase steels as a case study, we demonstrate how both methods effectively segment intricate microstructural details, including martensite, ferrite, and damage sites, for subsequent analysis. Either method shows substantial generalizability across a range of image sizes and conditions, including heat-treated microstructures with different phase configurations. The nnU-Net excels in mapping large image areas. Conversely, the GAN-based method performs reliably on smaller images, providing greater step-by-step control and flexibility over the segmentation process. This study highlights the benefits of segmented microstructural data for various purposes, such as calculating phase fractions, modelling material behaviour through finite element simulation, and conducting geometrical analyses of damage sites and the local properties of their surrounding microstructure.

cond-mat.mtrl-sci↗