TY - JOUR A1 - Valdestilhas, Andre A1 - Bayerlein, Bernd A1 - Moreno Torres, Benjami A1 - Zia, Ghezal Ahmad Jan A1 - Muth, Thilo T1 - The Intersection Between Semantic Web and Materials Science 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 - 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 - Zia, Ghezal Ahmad Jan 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 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 - 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 -