TY - CONF A1 - Zia, Ghezal Ahmad Jan A1 - Torres, Benjami Moreno A1 - Skrotzki, Birgit A1 - Bayerlein, Bernd T1 - Quantitative Precipitate Analysis of an Age-Hardenable Aluminium Alloy Using a Deep Learning Approach N2 - Mechanical properties of metals and their alloys are strongly governed by their microstructure. The nanometer-sized precipitates in hardenable wrought aluminium alloys, which can be controlled by heat treatment, act as obstacles to dislocation movement within the material and are critical to the mechanical performance of the component, in this case a radial compressor wheel of a ships’ engine. TEM-based image analysis is essential for the study to investigate the microstructural changes (precipitation coarsening) that occur as a result of ageing at elevated temperatures. T2 - MSE 2022 CY - Darmstadt, Germany DA - 27.09.2022 KW - Aluminium Alloy KW - Deep Learning KW - TEM Image PY - 2022 UR - https://dgm.de/mse/2022/ AN - OPUS4-55949 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Valdestilhas, Andre A1 - Bayerlein, Bernd A1 - Moreno Torres, Benjamin A1 - Jan Zia, Ghezal Ahmad A1 - Muth, Thilo T1 - The Intersection Between Semantic Web and Materials Science JF - Advanced Intelligent Systems N2 - The application and benefits of Semantic Web Technologies (SWT) for managing, sharing, and (re-)using of research data are demonstrated in implementations in the field of Materials Science and Engineering (MSE). However, a compilation and classification are needed to fully recognize the scattered published works with its unique added values. Here, the primary use of SWT at the interface with MSE is identified using specifically created categories. This overview highlights promising opportunities for the application of SWT to MSE, such as enhancing the quality of experimental processes, enriching data with contextual information in knowledge graphs, or using ontologies to perform specific queries on semantically structured data. While interdisciplinary work between the two fields is still in its early stages, a great need is identified to facilitate access for nonexperts and develop and provide user-friendly tools and workflows. The full potential of SWT can best be achieved in the long term by the broad acceptance and active participation of the MSE community. In perspective, these technological solutions will advance the field of MSE by making data FAIR. Data-driven approaches will benefit from these data structures and their connections to catalyze knowledge generation in MSE. KW - Linked open data KW - Materials science KW - Ontology KW - Semantic web PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-575506 DO - https://doi.org/10.1002/aisy.202300051 PB - Wiley-VCH GmbH CY - Weinheim AN - OPUS4-57550 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 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 JF - Journal of Cleaner Production 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 - JOUR A1 - Völker, Christoph A1 - Moreno Torres, Benjami A1 - Zia, Ghezal Ahmad A1 - Rug, Tehseen A1 - Firdous, Rafia A1 - Böhmer, Felix A1 - Stephan, Dietmar A1 - Kruschwitz, Sabine T1 - Presenting SLAMD – A Sequential Learning Based Software for the Inverse Design of Sustainable Cementitious Materials JF - NanoWorld Journal N2 - In recent decades, the number of components in concrete has grown, particularly in formulations aimed at reducing carbon footprints. Innovations include diverse binders, supplementary cementitious materials, activators, concrete admixtures, and recycled aggregates. These developments target not only the enhancement of material properties but also the mitigation of the ecological and economic impacts of concrete — the most extensively used material by humankind. However, these advancements also introduce a greater variability in the composition of raw materials. The material’s behavior is significantly influenced by its nanoscale properties, which can pose challenges in accurate characterization. Consequently, there’s an increasing need for experimental tuning of formulations. This is accompanied by a more inconsistent composition of raw materials, which makes an experimental tuning of formulations more and more necessary. However, the increased complexity in composition presents a challenge in finding the ideal formulation through trial and error. Inverse design (ID) techniques offer a solution to this challenge by allowing for a comprehensive search of the entire design space to create new and improved concrete formulations. In this publication, we introduce the concept of ID and demonstrate how our open-source app “SLAMD” provides all necessary steps of the workflow to adapt it in the laboratory, lowering the application barriers. The intelligent screening process, guided by a predictive model, leads to a more efficient and effective data-driven material design process resulting in reduced carbon footprint and improved material quality while considering socio-economic factors in the materials design. KW - Sustainable concrete KW - Machine learning optimization KW - Inverse design techniques KW - Scientific software KW - Data-driven material design PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-589993 DO - https://doi.org/10.17756/nwj.2023-s2-032 VL - 9 SP - 180 EP - 187 AN - OPUS4-58999 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 JF - Integrating Materials and Manufacturing Innovation 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 - 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 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 JF - Digital Discovery 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 -