TY - CONF A1 - Seifert, Lando T1 - Das Meerwasserlabor am Eidersperrwerk N2 - Das Meerwasserlabor (Laborcontainer) ist eine Remote-fähiges Elektrochemie-Labor mit entfeuchtungsfähiger Klimatisierung in dem eine Vielfalt an experimentellen Möglichkeiten besteht. Das Alleinstellungsmerkmal ist der Meerwasser-Bypass, durch den eine Besiedlung und Erhalt von Salzwasser- bzw. Brackwasserbewuchs (Bakterien, Algen, Tiere, Pilze, etc.) möglich ist. Der jährliche Verlauf der Meeresumwelt wird dadurch abgebildet. T2 - Leitprojekt H2Mare Verbundtreffen PtX-Wind & TransferWind CY - Frankfurt am Main, Germany DA - 05.12.2024 KW - Offshore Corrosion KW - Marine Corrosion KW - Corrosion KW - Green Hydrogen PY - 2024 AN - OPUS4-62054 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ryll, Tom William T1 - Researching automation of gypsum recycling N2 - This poster illustrates first successful experiments of recycling gypsum in hypersaline solutions with quantification via Raman-spectroscopy. To furhter enhancements include an automation setup, that was developed to gain in-situ measurements and open a pathway for batch-conversions and upscaling. T2 - BESSY@HZB User Meeting CY - Berlin, Germany DA - 12.12.2024 KW - Recycling KW - Gypsum KW - Raman-spectroscopy KW - Automation PY - 2024 AN - OPUS4-62292 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Naik, Aakash T1 - Material property predictions by incorporating quantum chemical bonding information N2 - Interactions between constituent atoms in crystalline materials have been shown to influence the properties of materials, such as elasticity, ionic and thermal conductivity, etc.[1–3] These interactions between constituent atoms, often quantified as bond strengths, can be extracted from crystalline materials using density-based[4], energy-based[5], and orbital-based methods. LOBSTER[6] is a software that relies on the orbital-based method to extract such bonding information by projecting the plane wave-based wave functions of modern density functional theory computations (DFT) onto a local atomic orbital basis. To garner a better understanding of how this bonding information relates to material properties on a larger scale, machine learning seems an obvious choice. However, for such data-driven studies, large quantities of data that are systematically generated, validated, and post-processed (feature engineering) in a form suitable for input in state-of-the-art ML models are often needed.[7] Here, we first present a workflow implemented in atomate2[8] that can generate such bonding-related data using the LOBSTER program with minimal user input and a post-processing tool, LobsterPy[9], which can summarize and engineer features that could be directly used as input for ML studies. Lastly, we demonstrate the utility of these newly generated features by building a simple machine-learned model to predict harmonic phonon properties using the bonding dataset[10] generated by us for 1500 materials. We find a clear correlation between the bonding information and the phonon property. T2 - STC 2024 CY - Braunschweig, Germany DA - 02.09.2024 KW - Bonding analysis KW - Machine learning KW - Feature engineering PY - 2024 AN - OPUS4-61130 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ertural, Christina T1 - Phonon-accurate machine-learning potentials from automated workflows N2 - Data-driven materials design aims to predict and optimize material properties, such as stability and thermal conductivity, which are influenced by vibrational behavior. Approaches like DFT are computationally demanding and have limitations for phonon calculations. Machine learning-driven interatomic potentials (MLIP), like the Gaussian approximation potential (GAP), offer a more efficient alternative.1–8 We developed a Python workflow to automate MLIP generation using the Materials Project database.9 DFT computations, MLIP fitting and benchmark steps are automated.10,11 This approach accelerates phonon calculations and supports testing different data generation strategies and hyperparameters, and further validation12 is planned. Our goal is to provide open-source code and share these potentials. T2 - Faraday Discussions: Data-driven discovery in the chemical sciences CY - Oxford, UK DA - 10.09.2024 KW - Interatomic potentials KW - Machine learning KW - Phonons KW - Thermoelectrics PY - 2024 AN - OPUS4-61061 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Naik, Aakash T1 - Enhancing material property predictions using quantum chemical bonding descriptors N2 - The properties of crystalline materials, such as elasticity, ionic conductivity, and thermal conductivity, are influenced by interactions between their constituent atoms.[1–3] These interactions, which are often quantified in terms of bond strength, can be extracted from crystalline materials using density-based[4], energy-based[5] and orbital-based methods. LOBSTER[6] is a software that relies on the orbital-based method to extract such bonding information by projecting the plane wave-based wave functions of modern density functional theory computations (DFT) onto a local atomic orbital basis. To garner a better understanding of how this bonding information relates to material properties on a larger scale, machine learning seems an obvious choice. However, for such data-driven studies, large quantities of data need to be systematically generated, validated, and post-processed (e.g., by feature engineering), as they can only then be used as input for state-of-the-art ML models. We have, therefore, previously developed workflows for high-throughput bonding analysis[7]. In this work, we use the results[8] from high-throughput LOBSTER calculations using our workflows to generate bonding-based features. To extract such features from the LOBSTER computations, we use our package LobsterPy.[9] The importance of these features is then tested by employing them in several state-of-the-art ML algorithms and architectures to predict the mechanical and vibrational properties of crystalline materials. We show that including these bonding-based features alongside typical composition and structure-based features helps enhance the model’s predictive accuracy. T2 - 18th German Conference on Cheminformatics CY - Bad Soden am Taunus, Germany DA - 03.11.2024 KW - Bonding analysis KW - Machine learning KW - Materials Descriptors PY - 2024 AN - OPUS4-62217 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ueltzen, Katharina T1 - Unveiling the potential of the Kanamori-Goodenough-Anderson rules for magnetic property prediction N2 - Recently, machine learning of magnetic properties of transition metal compounds has attracted large interest due to environmental and availability issues of rare-earth-based functional magnetic materials. Surprisingly, bond-angle-derived features were not found to be relevant for magnetic structure prediction in previous studies using DFT-computed labels. This contrasts with a well-known magnetism heuristic, the Kanamori-Goodenough-Anderson (KGA) rules of superexchange. We review magnetic interaction trends within the MAGNDATA database of experimentally determined magnetic structures. Observed trends follow the KGA rules „of thumb“ and exceptions can be rationalized. We introduce a new, informative label for predicting magnetic structures that can be extended to magnetic sites and structures of arbitrary complexity. Bond-angle-derived features are found to be highly relevant for magnetic structure prediction. T2 - Faraday Discussion: Data-driven discovery in the chemical sciences CY - Oxford, UK DA - 10.09.2024 KW - Magnetism KW - High-throughput analysis KW - Machine learning KW - Transition metal compounds PY - 2024 AN - OPUS4-62250 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ueltzen, Katharina T1 - Revisiting the Kanamori-Goodenough-Anderson rules for magnetic property prediction N2 - Recently, machine learning of magnetic properties of transition metal compounds has attracted large interest due to environmental and availability issues of rare-earth-based functional magnetic materials. Surprisingly, bond-angle-derived features were not found to be relevant for magnetic structure prediction in previous studies using DFT-computed labels. This contrasts with a well-known magnetism heuristic, the Kanamori-Goodenough-Anderson (KGA) rules of superexchange. We review magnetic interaction trends within the MAGNDATA database of experimentally determined magnetic structures. Observed trends follow the KGA rules „of thumb“ and exceptions can be rationalized. We introduce a new, informative label for predicting magnetic structures that can be extended to magnetic sites and structures of arbitrary complexity. Bond-angle-derived features are found to be highly relevant for magnetic structure prediction. T2 - 60th Symposium on Theoretical Chemistry (STC 2024) CY - Braunschweig, Germany DA - 02.09.2024 KW - Magnetism KW - High-throughput analysis KW - Machine learning KW - Transition metal compounds PY - 2024 AN - OPUS4-62249 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Neumann, Patrick P. T1 - Outdoor Gas Plume Reconstructions: A Field Study with Aerial Tomography N2 - This paper outlines significant advancements in our previously developed aerial gas tomography system, now optimized to reconstruct 2D tomographic slices of gas plumes with enhanced precision in outdoor environments. The core of our system is an aerial robot equipped with a custom-built 3-axis aerial gimbal, a Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor for CH4 measurements, a laser rangefinder, and a wide-angle camera, combined with a state-of-the-art gas tomography algorithm. In real-world experiments, we sent the aerial robot along gate-shaped flight patterns over a semi-controlled environment with a static-like gas plume, providing a welldefined ground truth for system evaluation. The reconstructed cross-sectional 2D images closely matched the known ground truth concentration, confirming the system’s high accuracy and reliability. The demonstrated system’s capabilities open doors for potential applications in environmental monitoring and industrial safety, though further testing is planned to ascertain the system’s operational boundaries fully. T2 - 20th International Symposium on Olfaction and Electronic Nose CY - Grapevine, Texas, USA DA - 12.05.2024 KW - Aerial Robot KW - TDLAS KW - Gas Tomography KW - Plume PY - 2024 AN - OPUS4-60108 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Prabhakara, Prathik T1 - Ein synergistischer Ansatz zur Charakterisierung anisotroper Materialien mit Hilfe von Ultraschall und Mikrostrukturanalyse N2 - Es wird eine Studie zur Charakterisierung eines anisotropen Stahls vorgestellt, bei der Ultraschalluntersuchungen mit Mikrostrukturanalysen verbunden werden. Das Material weist hohe Festigkeit und Korrosionsbeständigkeit auf, zugleich ist mit anisotropen Eigenschaften die mechanischen und betrieblichen Eigenschaften beeinflussen zu rechnen. Vorläufige Ergebnisse lassen vermuten, dass weitere Untersuchungen notwendig sind, um die Fähigkeiten und Grenzen des Materials genau zu bestimmen. Es wird ein systematischer Ansatz mit Array- Prüfköpfen, Time-of-Flight Diffraction (TOFD) Technik und mikrostrukturellen Untersuchungen angewendet, um die Wechselwirkung zwischen Anisotropie und Mikrostruktur des Stahls zu analysieren. Ultraschallprüfungen mit der TOFD-Technik und in Tauchtechnik liefern Einblicke in das anisotrope Verhalten des Werkstoffes, einschließlich entsprechenden Kornorientierung, Dämpfung und Schallgeschwindigkeitsvariation. Diese Messungen führen in Verbindung mit mikrostrukturellen Analysen zu einem tieferen Verständnis des Materialverhaltens. Unser Hauptziel ist es, ein Framework zu erstellen, welches die Ultraschallantwort anisotroper Materialien mit ihren mikroskopischen Struktureigenschaften verbindet. Die vorgestellte Methodik ermöglicht eine zerstörungsfreie und zügige Bewertung der Materialintegrität, was besonders bei der Anwendung von Hochleistungsmaterialien relevant ist. Durch diesen integrativen Ansatz werden verschiedener Charakterisierungsmethoden kombiniert, um ein umfassenderes Materialverständnis zu erreichen. T2 - DGZfP-Jahrestagung 2024 CY - Osnabrück, Germany DA - 06.05.2024 KW - Ultrasonic Testing KW - Time-offlight Diffraction (TOFD) KW - Microstructure Analysis KW - Non-Destructive Testing PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-600122 UR - https://www.ndt.net/?id=29535 AN - OPUS4-60012 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Reuter, T. T1 - Measurement-based Detector Characteristics for Digital Twins in aRTist 2 N2 - Various software products for the simulation of industrial X-ray radiography have been developed in recent years (e.g., aRTist 2, CIVA CT, Scorpius XLab, SimCT, Wilcore) and their application potential has been shown in numerous works. However, full systematic approaches to characterise a specific CT system for these simulation software products to obtain a truthful digital twin are still missing. In this contribution, we want to present two approaches to obtain realistic grey values in X-ray projections in aRTist 2 simulations based on measured projections. In aRTist 2, the displayed grey value of a pixel is based on the energy density incident on that pixel. The energy density is calculated based on the X-ray tube spectrum, the attenuation between source and detector as well as an energy-dependent sensitivity curve of the detector. The first approach presented in this contribution uses the sensitivity curve as a free modelling parameter. We measured the signal response at different thicknesses of Al EN-AW6082 at different tube voltages (i.e., different tube spectra). We then regarded the grey values displayed by these projections as a data regression respectively an optimisation problem and obtained the sensitivity curve that is best able to reproduce the measured behaviour in aRTist 2. The resulting sensitivity curve does not necessarily hold physical meaning but is able to simulate the real system behaviour in the simulation software. The second approach presented in this contribution is to estimate the sensitivity curve based on assumptions about the characteristics of the scintillation detector (e.g., scintillator material, scintillator thickness and signal processing characteristics). For this approach, a linear response function (linear relationship between the deposited energy per pixel and the resulting grey value) is assumed. If the detector characteristics, which affect the simulated deposited energy, are properly modelled, the slope and offset of the response function to match the measured grey values should be the same for different tube spectra. As the offset is constant and given by the grey values measured at no incident radiation, the slope is the remaining parameter to evaluate the success of the detector modelling. We therefore adapted the detector characteristics by changing the detector setup until the slope was nearly the same for all measured tube spectra. We are aware that the resulting parameters of the scintillator material and thickness might not be the real ones, but with those modelling parameters we are able to simulate realistic grey values in aRTist 2. Both of those approaches could potentially be a step forward to a full systematic approach for a digital twin of a real CT system in aRTist 2. T2 - 20th World Conference on Non-Destructive Testing (WCNDT 2024) CY - Incheon, South Korea DA - 27.05.2024 KW - Computed Tomography KW - Digital Twin KW - Simulation PY - 2024 AN - OPUS4-61776 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -