arXiv subjects

Ulrich Kerzel

Publications and source records attributed to Ulrich Kerzel.

11 recordsLinked to original sources

arXiv2026

EBSDmagus: Managing Multi-Stage Dynamical Electron Backscatter Diffraction Simulations

Dynamical electron backscatter diffraction (EBSD) simulations increasingly support systematic studies of how material and experimental parameters affect diffraction patterns. Investigations that combine composition and crystal structure, including changes in atomic order, with accelerating voltage, temperature, and many orientations or detector conditions can require large sets of related calculations. Several packages provide dynamical EBSD simulation; we use the open-source, scriptable EMsoft programs because they are well suited to large studies on high-performance computing systems. An EMsoft investigation nevertheless spans separate configuration, structure, scattering, and orientation files and several dependent calculation stages. As variations multiply, manual preparation becomes difficult to check, interruptions obscure which results remain usable, and the origin of individual patterns becomes laborious to reconstruct. With EBSDmagus, researchers define fixed and varying parameters once. The software prepares the required EMsoft calculations, reuses compatible intermediate results, and checks that the generated files represent the requested investigation before execution. After execution, it checks the expected outputs and, following an interruption, resumes only unresolved calculations while preserving completed work. A portable run record links each output to its inputs and calculation history, providing the provenance needed when the results are deposited as findable, accessible, interoperable, and reusable (FAIR) data. We assess this approach using a completed representative calculation, a larger study prepared and checked before submission, two observed cluster interruptions, and a controlled job cancellation. During recovery, EBSDmagus retained completed calculations and resubmitted only incomplete work.

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

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

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↗

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

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↗

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↗

arXiv2023

Rate dependence of damage formation in metallic-intermetallic Mg-Al-Ca composites

We study a cast Mg-4.65Al-2.82Ca alloy with a microstructure containing $α$-Mg matrix reinforced with a C36 Laves phase skeleton. Such ternary alloys are targeted for elevated temperature applications in automotive engines since they possess excellent creep properties. However, in application, the alloy may be subjected to a wide range of strain rates and in material development, accelerated testing is often of essence. It is therefore crucial to understand the effect of such rate variations. Here, we focus on their impact on damage formation. Due to the locally highly variable skeleton forming the reinforcement in this alloy, we employ an analysis based on high resolution panoramic imaging by scanning electron microscopy coupled with automated damage analysis by deep learning-based object detection and classification convolutional neural network algorithm (YOLOV5). We find that with decreasing strain rate the dominant damage mechanism for a given strain level changes: at a strain rate of $5\cdot10^{-4}/s$ the evolution of microcracks in the C36 Laves phase governs damage formation. However , when the strain rate is decreased to $5\cdot10^{-6}/s$, interface decohesion at the $α$-Mg/Laves phase interfaces becomes equally important. We also observe a change in crack orientation indicating an increasing influence of plastic co-deformation of the α-Mg matrix and Laves phase. We attribute this transition in leading damage mechanism to thermally activated processes at the interface.

cond-mat.mtrl-sci↗

arXiv2023

Three-Dimensional Damage Characterisation in Dual Phase Steel using Deep Learning

High performance sheet metals with a multi-phase microstructure suffer from deformation induced damage formation during forming in the constituent phases but importantly also where these intersect. To capture damage in terms of the physical processes in three dimensions (3D) and its stochastic nature during deformation, two challenges remain to be tackled: First, bridging high resolution analysis towards large scales to consider statistical data and, second, characterising in 3D with a resolution appropriate for sub-micron sized voids at a large scale. Here, we present how this can be achieved using panoramic scanning electron microscopy (SEM), metallographic serial sectioning, and deep-learning assisted automatic image analysis. This brings together the 3D evolution of active damage mechanisms with volumetric and environmental information for thousands of individual damage sites. We also assess potential surface preparation artefacts in 2D analyses. Overall, we find that for the material considered here, a dual phase (DP800) steel, martensite cracking is the dominant but not sole origin of deformation induced damage and that for a quantitative comparison of damage density, metallographic preparation can induce additional surface damage density far exceeding what is commonly induced between uniaxial straining steps.

cond-mat.mtrl-sci↗

arXiv2021

Cyclic Boosting -- an explainable supervised machine learning algorithm

Supervised machine learning algorithms have seen spectacular advances and surpassed human level performance in a wide range of specific applications. However, using complex ensemble or deep learning algorithms typically results in black box models, where the path leading to individual predictions cannot be followed in detail. In order to address this issue, we propose the novel "Cyclic Boosting" machine learning algorithm, which allows to efficiently perform accurate regression and classification tasks while at the same time allowing a detailed understanding of how each individual prediction was made.

cs.LG↗

arXiv2019

High-resolution, yet statistically relevant, analysis of damage in DP steel using artificial intelligence

High performance materials, from natural bone over ancient damascene steel to modern superalloys, typically possess a complex structure at the microscale. Their properties exceed those of the individual components and their knowledge-based improvement therefore requires understanding beyond that of the components' individual behaviour. Electron microscopy has been instrumental in unravelling the most important mechanisms of co-deformation and in-situ deformation experiments have emerged as a popular and accessible technique. However, a challenge remains: to achieve high spatial resolution and statistical relevance in combination. Here, we overcome this limitation by using panoramic imaging and machine learning to study damage in a dual-phase steel. This high-throughput approach not only gives us strain and microstructure dependent insights across a large area of this heterogeneous material, but also encourages us to expand current research past interpretation of exemplary cases of distinct damage sites towards the less clear-cut reality.

cond-mat.mtrl-sci↗