TY - JOUR A1 - Lai, W. W.L. A1 - Chang, R.K.W. A1 - Völker, Christoph A1 - Cheung, B. W.Y. T1 - GPR wave dispersion for material characterization N2 - This paper studies the dispersion of GPR wave’s phase velocity at different wideband frequencies in plywood and concrete with varying moisture content. This study makes use of two GPR antennas with 2 GHz centre frequency operating in wide angle reflection and refraction (WARR) mode and with computation of spectral analysis of the surface wave (SASW). Computation of phase velocities is based on the Acquisition of the cross-power spectrum and phase unwrap of two distorted ground waves at positions Closer to and farther away from the transmitting antenna. The velocities of the ground waves are found to experience greater dispersion in low frequency regimes within the effective frequency bandwidths determined and thresholded by time–frequency analysis (TFA) and coherence plotting of the ground waves. This study validates not only the methodology, but also identifies the optimal distance between the first (Rx1) and second (Rx2) receivers as k/2, which is based on a fixed transmitter (Tx) minus the first Receiver (Rx1) distance. It serves as an indication of changeable separation distance when other lower frequency GPR is used because the distances of Tx-Rx1 and Rx1-Rx2 are wavelength dependent and thus also frequency dependent. Effects of moisture contents and chloride contamination in concrete were also characterized according to the dispersion plots . that wave traelling in lower frequencies in GPR wave is much decelerated than that in higher frequencies. This research also contributes to the building of the ‘‘GPRWARR machine” suggested in Annan and Jackson (2017) [1], within which the effects of wave Dispersion on phase velocity can be inversely modelled to characterize variations in the material properties of infrastructure as a means of detecting surface damage. KW - Ground penetrating radar KW - Wide angle reflection and refraction PY - 2021 DO - https://doi.org/10.1016/j.conbuildmat.2021.122597 SN - 0950-0618 VL - 282 SP - 122597 PB - Elsevier Ltd. AN - OPUS4-52197 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Völker, Christoph A1 - Firdous, R. A1 - Kruschwitz, Sabine A1 - Stephan, D. T1 - Sequential learning to accelerate discovery of alkali-activated binders N2 - Alkali-activated binders (AAB) can provide a clean alternative to conventional cement in terms of CO2 emissions. However, as yet there are no sufficiently accurate material models to effectively predict the AAB properties, thus making optimal mix design highly costly and reducing the attractiveness of such binders. This work adopts sequential learning (SL) in high-dimensional material spaces (consisting of composition and processing data) to find AABs that exhibit desired properties. The SL approach combines machine learning models and feedback from real experiments. For this purpose, 131 data points were collected from different publications. The data sources are described in detail, and the differences between the binders are discussed. The sought-after target property is the compressive strength of the binders after 28 days. The success is benchmarked in terms of the number of experiments required to find materials with the desired strength. The influence of some constraints was systematically analyzed, e.g., the possibility to parallelize the experiments, the influence of the chosen algorithm and the size of the training data set. The results show the advantage of SL, i.e., the amount of data required can potentially be reduced by at least one order of magnitude compared to traditional machine learning models, while at the same time exploiting highly complex information. This brings applications in laboratory practice within reach. KW - Alkali-activated binders KW - Machine learning KW - Sequential learning KW - Materials by design KW - Materials informatics PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-531376 DO - https://doi.org/10.1007/s10853-021-06324-z SN - 0022-2461 SN - 1573-4803 VL - 56 SP - 15859 EP - 15881 PB - Springer CY - Dordrecht AN - OPUS4-53137 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Jablonka, Kevin Maik A1 - Ai, Qianxiang A1 - Al-Feghali, Alexander A1 - Badhwar, Shruti A1 - Bocarsly, Joshua D. A1 - Bran, Andres M. A1 - Bringuier, Stefan A1 - Brinson, L. Catherine A1 - Choudhary, Kamal A1 - Circi, Defne A1 - Cox, Sam A1 - de Jong, Wibe A. A1 - Evans, Matthew L. A1 - Gastellu, Nicolas A1 - Genzling, Jerome A1 - Gil, María Victoria A1 - Gupta, Ankur K. A1 - Hong, Zhi A1 - Imran, Alishba A1 - Kruschwitz, Sabine A1 - Labarre, Anne A1 - Lála, Jakub A1 - Liu, Tao A1 - Ma, Steven A1 - Majumdar, Sauradeep A1 - Merz, Garrett W. A1 - Moitessier, Nicolas A1 - Moubarak, Elias A1 - Mouriño, Beatriz A1 - Pelkie, Brenden A1 - Pieler, Michael A1 - Ramos, Mayk Caldas A1 - Ranković, Bojana A1 - Rodriques, Samuel G. A1 - Sanders, Jacob N. A1 - Schwaller, Philippe A1 - Schwarting, Marcus A1 - Shi, Jiale A1 - Smit, Berend A1 - Smith, Ben E. A1 - Van Herck, Joren A1 - Völker, Christoph A1 - Ward, Logan A1 - Warren, Sean A1 - Weiser, Benjamin A1 - Zhang, Sylvester A1 - Zhang, Xiaoqi A1 - Zia, Ghezal Ahmad Jan A1 - Scourtas, Aristana A1 - Schmidt, K. J. A1 - Foster, Ian A1 - White, Andrew D. A1 - Blaiszik, Ben T1 - 14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon N2 - Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines. KW - Large Language model KW - Hackathon KW - Concrete KW - Prediction KW - Inverse Design KW - Orchestration PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-589961 DO - https://doi.org/10.1039/d3dd00113j VL - 2 IS - 5 SP - 1233 EP - 1250 PB - Royal Society of Chemistry (RSC) AN - OPUS4-58996 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Moreno Torres, Benjami A1 - Völker, Christoph A1 - Firdous, Rafia T1 - Concreting a sustainable future: A dataset of alkali-activated concrete and its properties N2 - This data article introduces a dataset comprising 1630 alkali-activated concrete (AAC) mixes, compiled from 106 literature sources. The dataset underwent extensive curation to address feature redundancy, transcription errors, and duplicate data, yielding refined data ready for further data-driven science in the field of AAC, where this effort constitutes a novelty. The carbon footprint associated with each material used in the AAC mixes, as well as the corresponding CO2 footprint of every mix, were approximated using two published articles. Serving as a foundation for future expansions and rigorous data applications, this dataset enables the characterization of AAC properties through machine learning algorithms or as a benchmark for performance comparison among different formulations. In summary, the dataset provides a resource for researchers focusing on AAC and related materials and offers insights into the environmental benefits of substituting traditional Portland concrete with AAC. KW - Multidisciplinary KW - Data Set KW - Alkali Activated Concrete KW - Data driven design PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-589974 DO - https://doi.org/10.1016/j.dib.2023.109525 SN - 2352-3409 VL - 50 SP - 1 EP - 9 PB - Elsevier B.V. AN - OPUS4-58997 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Völker, Christoph A1 - Moreno Torres, Benjami A1 - Rug, Tehseen A1 - Firdous, Rafia A1 - Zia, Ghezal Ahmad Jan A1 - Lüders, Stefan A1 - Lisdero Scaffino, Horacio A1 - Höpler, Michael A1 - Böhmer, Felix A1 - Pfaff, Matthias A1 - Stephan, Dietmar A1 - Kruschwitz, Sabine T1 - Data driven design of alkali-activated concrete using sequential learning N2 - This paper presents a novel approach for developing sustainable building materials through Sequential Learning. Data sets with a total of 1367 formulations of different types of alkali-activated building materials, including fly ash and blast furnace slag-based concrete and their respective compressive strength and CO2-footprint, were compiled from the literature to develop and evaluate this approach. Utilizing this data, a comprehensive computational study was undertaken to evaluate the efficacy of the proposed material design methodologies, simulating laboratory conditions reflective of real-world scenarios. The results indicate a significant reduction in development time and lower research costs enabled through predictions with machine learning. This work challenges common practices in data-driven materials development for building materials. Our results show, training data required for data-driven design may be much less than commonly suggested. Further, it is more important to establish a practical design framework than to choose more accurate models. This approach can be immediately implemented into practical applications and can be translated into significant advances in sustainable building materials development. KW - Sustainable building materials KW - Sequential learning KW - Data-driven materials design KW - Alkali-activated building materials PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-584376 UR - https://www.sciencedirect.com/science/article/pii/S095965262302379X DO - https://doi.org/10.1016/j.jclepro.2023.138221 SN - 0959-6526 SN - 1879-1786 VL - 418 SP - 1 EP - 13 PB - Elsevier CY - Amsterdam AN - OPUS4-58437 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Völker, Christoph A1 - John, Elisabeth A1 - Firdous, Rafia A1 - Hirsch, Tamino A1 - Kaczmarek, Daria A1 - Ziesack, Kevin A1 - Buchwald, Anja A1 - Stephan, Dietmar A1 - Kruschwitz, Sabine ED - Ferrara, Liberato ED - Muciaccia, Giovanni ED - di Summa, Davide T1 - Beyond Theory: Pioneering AI-Driven Materials Design in the Sustainable Building Material Lab N2 - This work focuses on Artificial Intelligence (AI)-driven materials design, addressing the challenge of improving the sustainability of building materials amid complex formulations. These formulations involve various components, such as binders, additives, and recycled aggregates, necessitating a balance between environmental impact and performance. Traditional experimental methods often fall short in managing the complexity of material composition, hindering fast enough development of optimal solutions. Our research explores complex composition materials design through a comprehensive, comparative lab study between Data-Driven Design, using SLAMD - an open-source AI materials design tool, and traditional Design of Experiments (DOE). We aimed to develop a high-performance, alkali-activated material using secondary precursors, aiming for a compressive strength exceeding 100 MPa after 7-days. The findings reveal that AI-driven design outperforms DOE in development speed and material quality, successfully identif. T2 - 4 RILEM Spring Convention and Conference on advanced construction materials and processes for a carbon neutral society 2024 CY - Milano, Italy DA - 07.04.2024 KW - Secondary Raw Materials KW - Data-Driven Design KW - Sequential Learning KW - Design of Experiments KW - Alkali-Activated Binder PY - 2024 SN - 978-3-03170281-5 DO - https://doi.org/10.1007/978-3-031-70281-5_31 SN - 2211-0852 VL - 2 SP - 274 EP - 282 PB - Springer AN - OPUS4-61662 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Zia, Ghezal Ahmad Jan A1 - Völker, Christoph A1 - Moreno Torres, Benjami A1 - Kruschwitz, Sabine T1 - An Adaptive Upscaling Approach for Assessing Materials’ Circularity Potential with Non-destructive Testing (NDT) N2 - Advancing towards a circular economy necessitates the efficient reuse and maintenance of structural materials, which relies on accurate, non-damaging condition assessments. This paper introduces an innovative AI-driven adaptive sampling (AS) technique integrated with Non-Destructive Testing (NDT) to optimize this process. AS focuses on critical data points, reducing the amount of data needed for precise assessments—evidenced by our method requiring on average only 7 samples for Logistic Regression and 8 for Random Forest, contrasted with 29 for traditional sampling. By reducing the necessity for extensive data collection, our method not only streamlines the assessment process but also significantly contributes to the sustainability goals of the circular economy. These goals include resource efficiency, waste reduction, and material reuse. Efficient condition assessments promote infrastructure longevity, reducing the need for new materials and the associated environmental impact. The circular economy aims to create a sustainable system where resources are reused, and waste is minimized. This is achieved by extending the lifecycle of materials, reducing the environmental footprint, and promoting recycling and reuse. Longevity directly contributes to the circular economy by maximizing the utility and lifespan of existing materials and structures. Longer-lasting infrastructure means fewer resources are needed for repairs or replacements, leading to reduced material consumption and waste generation. This aligns with the circular economy's principles of sustainability and resource efficiency. This research not only advances the field of structural health monitoring but also aligns with the broader objective of enhancing sustainable construction practices within the circular economy framework. T2 - Rilem Spring Convention CY - Milano, Italy DA - 09.04.2024 KW - Adaptive Sampling KW - Random Sampling KW - Machine Learning KW - Non-Destructive Testing KW - Condition Assessment KW - Circular Economy PY - 2024 SN - 978-3-031-70277-8 DO - https://doi.org/10.1007/978-3-031-70277-8_38 SN - 2211-0844 VL - 55 SP - 330 EP - 338 PB - Springer Nature Switzerland CY - Switzerland AN - OPUS4-62458 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Kruschwitz, Sabine A1 - Völker, Christoph A1 - Zia, Ghezal Ahmad Jan A1 - Moreno Torres, Benjami A1 - Hartmann, Timo ED - Ferrara, L. ED - Muciaccia, G. ED - di Summa, D. T1 - REINCARNATE: Shaping a Sustainable Future in Construction Through Digital Innovation N2 - We introduce the REINCARNATE project, funded by the European Union’s Horizon Europe program, to boost circularity by merging digital innovations with practical applications and a focus on material reuse. The heart of REINCARNATE is the Circular Potential Information Model (CP-IM), a digital platform designed to assess and enhance the recyclability of construction materials, construction products, and buildings. The CP-IM integrates advanced technologies such as digital twins, AI, and robotics to revolutionize the handling of construction waste, turning it into valuable resources and cutting the environmental footprint of the sector. Among its features are digital tracing, material durability predictions, and CO2 reduction materials design. These are showcased in eleven European demonstration projects, highlighting the practical benefits of these technologies in reducing construction waste and CO2 emissions by up to 80% and 70% respectively. REINCARNATE aims to marry innovation with real-world application, providing the construction industry with strategies for sustainable and circular practices. T2 - 4 RILEM Spring Convention and Conference on advanced construction materials and processes for a carbon neutral society 2024 CY - Milano, Italy DA - 07.04.2024 KW - Digital construction KW - Construction sustainability KW - European project KW - Llife cycle KW - Recycled materials PY - 2024 SN - 978-3-031-70280-8 SN - 978-3-03170281-5 SN - 978-3-031-70283-9 DO - https://doi.org/10.1007/978-3-031-70281-5_32 SN - 2211-0844 SN - 2211-0852 N1 - Serientitel: RILEM Bookseries – Series title: RILEM Bookseries VL - 56 IS - 2 SP - 283 EP - 291 PB - Springer CY - Cham AN - OPUS4-61661 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Moreno Torres, Benjami A1 - Völker, Christoph A1 - Munsch, Sarah Mandy A1 - Hanke, T. A1 - Kruschwitz, Sabine ED - Tosti, F. T1 - An Ontology-Based Approach to Enable Data-Driven Research in the Field of NDT in Civil Engineering N2 - Although measurement data from the civil engineering sector are an important basis for scientific analyses in the field of non-destructive testing (NDT), there is still no uniform representation of these data. An analysis of data sets across different test objects or test types is therefore associated with a high manual effort. Ontologies and the semantic web are technologies already used in numerous intelligent systems such as material cyberinfrastructures or research databases. This contribution demonstrates the application of these technologies to the case of the 1H nuclear magnetic resonance relaxometry, which is commonly used to characterize water content and porosity distri-bution in solids. The methodology implemented for this purpose was developed specifically to be applied to materials science (MS) tests. The aim of this paper is to analyze such a methodology from the perspective of data interoperability using ontologies. Three benefits are expected from this ap-proach to the study of the implementation of interoperability in the NDT domain: First, expanding knowledge of how the intrinsic characteristics of the NDT domain determine the application of semantic technologies. Second, to determine which aspects of such an implementation can be improved and in what ways. Finally, the baselines of future research in the field of data integration for NDT are drawn. KW - Ontology Engineering KW - Interoperability KW - Data-integration KW - NMR relaxometry KW - materials informatics PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-529716 DO - https://doi.org/10.3390/rs13122426 SN - 2072-4292 N1 - Geburtsname von Munsch, Sarah Mandy: Nagel, S. M. - Birth name of Munsch, Sarah Mandy: Nagel, S. M. VL - 13 IS - 12 SP - 2426 PB - Multidisciplinary Digital Publishing Institute (MDPI) CY - Basel, Switzerland AN - OPUS4-52971 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Munsch, Sarah Mandy A1 - Telong, Melissa A1 - Grobla, Lili A1 - Schumacher, Katrin A1 - Völker, Christoph A1 - Yared, Kaleb A1 - Kruschwitz, Sabine ED - Ferrara, Liberato ED - Muciaccia, Giovanni ED - di Summa, Davide T1 - Study on the Predictability of Carbonation Resistance of Cementitous Materials Based on NMR Features and the Use of SLAMD N2 - This study explores the acceleration of material design in the concrete industry, focusing on improving carbonation resistance, a key factor in the durability of concrete structures. Traditional tests for carbonation resistance are lengthy, but with the construction industry aiming for sustainable production, finding a balance between carbonation resistance and CO2 footprint is crucial. Our research employs two innovative methods: 1. Applying the Sequential Learning App for Materials Discovery (SLAMD), an AI materials design framework, to an extensive dataset of real-world concrete compositions to selectively test materials that meet market demands: maximum durability, optimal eco-durability, and the best cost-durability trade-off. 2. Investigating 1H Nuclear Magnetic Resonance (NMR) relaxometry as a quick alternative for characterizing carbonation behavior, as it saves time compared to traditional tests and assesses the complete material’s pore space. Specific NMR features are then integrated into the material design model, with the model’s performance compared against traditional approaches. The results of our study are compelling, demonstrating that materials can be precisely tailored to meet specific requirements with minimal data points. This marks a significant stride in the concrete industry, indicating thatNMR-based, lowfidelity surrogate characterizations, combined with a focused, data-driven design approach, can substantially accelerate the development of durable, sustainable concrete mixtures. T2 - 4 RILEM Spring Convention and Conference on advanced construction materials and processes for a carbon neutral society 2024 CY - Milano, Italy DA - 07.04.2024 KW - Nuclear magnetic resonance KW - carbonation resistance KW - cement and concrete KW - SLAMD app KW - predictability PY - 2024 DO - https://doi.org/10.1007/978-3-031-70281-5_49 VL - 2 SP - 435 EP - 442 PB - Springer CY - Cham, Switzerland AN - OPUS4-61692 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Völker, Christoph A1 - Shokouhi, P. T1 - Clustering based multi sensor data fusion for honeycomb detection in concrete KW - Data fusion KW - Concrete evaluation KW - Honeycombing KW - Machine learning KW - Clustering KW - Density based clustering PY - 2015 DO - https://doi.org/10.1007/s10921-015-0307-7 SN - 0195-9298 SN - 1573-4862 VL - 34 IS - Article 32 SP - 1 EP - 10 PB - Plenum Press CY - New York, NY AN - OPUS4-35073 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Völker, Christoph A1 - Shokouhi, P. T1 - Multi sensor data fusion approach for automatic honeycomb detection in concrete PY - 2015 DO - https://doi.org/10.1016/j.ndteint.2015.01.003 SN - 0963-8695 VL - 71 SP - 54 EP - 60 PB - Butterworth-Heinemann CY - Oxford AN - OPUS4-35074 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Völker, Tobias A1 - Millar, Steven A1 - Strangfeld, Christoph A1 - Wilsch, Gerd T1 - Identification of type of cement through laser-induced breakdown spectroscopy N2 - The composition of concrete determines its resistance to various degradation mechanisms such as ingress of ions, carbonation or reinforcement corrosion. Knowledge of the composition of the hardened concrete is therefore helpful to assess the remaining service life of an existing structure or evaluate the damage observed during inspections. For example, for most existing concrete structures the type of cement originally used is not known and must therefore be determined afterwards. This paper presents a preliminary study on the application of laser-induced breakdown spectroscopy (LIBS) to identify the type of cement. For this purpose, ten different types of cement were investigated. For every type, three cement paste prisms were produced: (i) prisms dried, ground and pressed into tablets, (ii) prisms dried and (iii) prisms untreated. LIBS measurements were performed with a diode-pumped low energy laser (1064 nm, 3 mJ, 1.5 ns, 100 Hz) in combination with two compact spectrometers which cover the UV and NIR spectral range. A reduced subset of spectral features was used to build a classification model based on linear discriminant analysis. The results show that the classification of homogenized pressed cement powder samples provides a high accuracy, however, factors such as a different sample matrix and moisture content can affect the accuracy of the classification. The study demonstrates that LIBS is a promising tool to identify the type of cement. KW - Spectroscopy KW - LIBS KW - Cement KW - Classification KW - Identification PY - 2020 DO - https://doi.org/10.1016/j.conbuildmat.2020.120345 VL - 258 SP - 120345 PB - Elsevier Ltd. AN - OPUS4-51157 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Völker, Christoph A1 - Kruschwitz, Sabine A1 - Ebell, Gino T1 - A machine learning‑based data fusion approach for improved corrosion testing N2 - This work presents machine learning-inspired data fusion approaches to improve the non-destructive testing of reinforced concrete. The principal effects that are used for data fusion are shown theoretically. Their effectiveness is tested in case studies carried out on largescale concrete specimens with built-in chloride-induced rebar corrosion. The dataset consists of half-cell potential mapping, Wenner resistivity, microwave moisture and ground penetrating radar measurements. Data fusion is based on the logistic Regression algorithm. It learns an optimal linear decision boundary from multivariate labeled training data, to separate intact and defect areas. The training data are generated in an experiment that simulates the entire life cycle of chloride-exposed concrete building parts. The unique possibility to monitor the deterioration, and targeted corrosion initiation, allows data labeling. The results exhibit an improved sensitivity of the data fusion with logistic regression compared to the best individual method half-cell potential. KW - Corrosion KW - Potential mapping KW - Machine learning PY - 2019 DO - https://doi.org/10.1007/s10712-019-09558-4 SN - 1573-0956 SN - 0169-3298 VL - 41 IS - 3 SP - 531 EP - 548 PB - Springer Nature AN - OPUS4-48799 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Zia, Ghezal Ahmad Jan A1 - Hanke, Thomas A1 - Skrotzki, Birgit A1 - Völker, Christoph A1 - Bayerlein, Bernd T1 - Enhancing Reproducibility in Precipitate Analysis: A FAIR Approach with Automated Dark-Field Transmission Electron Microscope Image Processing N2 - AbstractHigh-strength aluminum alloys used in aerospace and automotive applications obtain their strength through precipitation hardening. Achieving the desired mechanical properties requires precise control over the nanometer-sized precipitates. However, the microstructure of these alloys changes over time due to aging, leading to a deterioration in strength. Typically, the size, number, and distribution of precipitates for a quantitative assessment of microstructural changes are determined by manual analysis, which is subjective and time-consuming. In our work, we introduce a progressive and automatable approach that enables a more efficient, objective, and reproducible analysis of precipitates. The method involves several sequential steps using an image repository containing dark-field transmission electron microscopy (DF-TEM) images depicting various aging states of an aluminum alloy. During the process, precipitation contours are generated and quantitatively evaluated, and the results are comprehensibly transferred into semantic data structures. The use and deployment of Jupyter Notebooks, along with the beneficial implementation of Semantic Web technologies, significantly enhances the reproducibility and comparability of the findings. This work serves as an exemplar of FAIR image and research data management. KW - Industrial and Manufacturing Engineering KW - General Materials Science KW - Automated image analysis KW - FAIR research data management KW - Reproducibility KW - microstructural changes PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-593905 DO - https://doi.org/10.1007/s40192-023-00331-5 SN - 2193-9772 SP - 1 EP - 15 PB - Springer Science and Business Media LLC CY - Heidelberg AN - OPUS4-59390 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bayerlein, Bernd A1 - Hanke, T. A1 - Muth, Thilo A1 - Riedel, Jens A1 - Schilling, Markus A1 - Schweizer, C. A1 - Skrotzki, Birgit A1 - Todor, A. A1 - Moreno Torres, Benjami A1 - Unger, Jörg F. A1 - Völker, Christoph A1 - Olbricht, Jürgen T1 - A Perspective on Digital Knowledge Representation in Materials Science and Engineering N2 - The amount of data generated worldwide is constantly increasing. These data come from a wide variety of sources and systems, are processed differently, have a multitude of formats, and are stored in an untraceable and unstructured manner, predominantly in natural language in data silos. This problem can be equally applied to the heterogeneous research data from materials science and engineering. In this domain, ways and solutions are increasingly being generated to smartly link material data together with their contextual information in a uniform and well-structured manner on platforms, thus making them discoverable, retrievable, and reusable for research and industry. Ontologies play a key role in this context. They enable the sustainable representation of expert knowledge and the semantically structured filling of databases with computer-processable data triples. In this perspective article, we present the project initiative Materials-open-Laboratory (Mat-o-Lab) that aims to provide a collaborative environment for domain experts to digitize their research results and processes and make them fit for data-driven materials research and development. The overarching challenge is to generate connection points to further link data from other domains to harness the promised potential of big materials data and harvest new knowledge. KW - Data infrastructures KW - Digital representations KW - Digital workflows KW - Knowledge graphs KW - Materials informatics KW - Ontologies KW - Vocabulary providers PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-546729 DO - https://doi.org/10.1002/adem.202101176 SN - 1438-1656 SP - 1 EP - 14 PB - Wiley-VCH GmbH CY - Weinheim AN - OPUS4-54672 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Karafiludis, Stephanos A1 - Standl, Jacob A1 - Ryll, Tom W. A1 - Schwab, Alexander A1 - Prinz, Carsten A1 - Wolf, Jakob B. A1 - Kruschwitz, Sabine A1 - Emmerling, Franziska A1 - Völker, Christoph A1 - Stawski, Tomasz M. T1 - High-Entropy Phosphate Synthesis: Advancements through Automation and Sequential Learning Optimization N2 - Transition metal phosphates (TMPs) are extensively explored for electrochemical and catalytical applications due to their structural versatility and chemical stability. Within this material class, novel high-entropy metal phosphates (HEMPs)─containing multiple transition metals combined into a single-phase structure─are particularly promising, as their compositional complexity can significantly enhance functional properties. However, the discovery of suitable HEMP compositions is hindered by the vast compositional design space and complex or very specific synthesis conditions. Here, we present a data-driven strategy combining automated wet-chemical synthesis with a Sequential Learning App for Materials Discovery (SLAMD) framework (Random Forest regression model) to efficiently explore and optimize HEMP compositions. Using a limited set of initial experiments, we identified multimetal compositions in a single-phase crystalline solid. The model successfully predicted a novel Co0.3Ni0.3Fe0.2Cd0.1Mn0.1 phosphate octahydrate phase, validated experimentally, demonstrating the effectiveness of the machine learning approach. This work highlights the potential of integrating automated synthesis platforms with data-driven algorithms to accelerate the discovery of high-entropy materials, offering an efficient design pathway to advanced functional materials. KW - Metal phosphates KW - High entropy KW - Sequential learning KW - Automated synthesis KW - MAP KW - Random forest KW - Machine learning PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-641554 DO - https://doi.org/10.1021/acs.cgd.5c00549 SN - 1528-7483 VL - 25 IS - 19 SP - 7989 EP - 8001 PB - American Chemical Society (ACS) CY - Washington, DC AN - OPUS4-64155 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -