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  <doc>
    <id>58999</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>180</pageFirst>
    <pageLast>187</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>9</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
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    <title language="eng">Presenting SLAMD – A Sequential Learning Based Software for the Inverse Design of Sustainable Cementitious Materials</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">NanoWorld Journal</parentTitle>
    <identifier type="doi">10.17756/nwj.2023-s2-032</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-589993</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Christoph Völker</author>
    <author>Benjami Moreno Torres</author>
    <author>Ghezal Ahmad Jan Zia</author>
    <author>Tehseen Rug</author>
    <author>Rafia Firdous</author>
    <author>Felix Böhmer</author>
    <author>Dietmar Stephan</author>
    <author>Sabine Kruschwitz</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sustainable concrete</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine learning optimization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Inverse design techniques</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Scientific software</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data-driven material design</value>
    </subject>
    <collection role="ddc" number="620">Ingenieurwissenschaften und zugeordnete Tätigkeiten</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
    <collection role="themenfelder" number="">Material</collection>
    <collection role="literaturgattung" number="">Verlagsliteratur</collection>
    <collection role="fulltextaccess" number="">Datei für die Öffentlichkeit verfügbar ("Open Access")</collection>
    <collection role="unnumberedseries" number="">Wissenschaftliche Artikel der BAM</collection>
    <collection role="institutes" number="">8.0 Abteilungsleitung und andere</collection>
    <collection role="themenfelder" number="">Materialdesign</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/58999/Published_Version_Presenting_SLAMD_Voelker_2023.pdf</file>
  </doc>
  <doc>
    <id>58437</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>13</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>418</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
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    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
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    <title language="eng">Data driven design of alkali-activated concrete using sequential learning</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Journal of Cleaner Production</parentTitle>
    <identifier type="url">https://www.sciencedirect.com/science/article/pii/S095965262302379X</identifier>
    <identifier type="doi">10.1016/j.jclepro.2023.138221</identifier>
    <identifier type="issn">0959-6526</identifier>
    <identifier type="issn">1879-1786</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-584376</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="date_peer_review">26.09.2023</enrichment>
    <enrichment key="PaperofMonth">1</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Christoph Völker</author>
    <author>Benjami Moreno Torres</author>
    <author>Tehseen Rug</author>
    <author>Rafia Firdous</author>
    <author>Ghezal Ahmad Jan Zia</author>
    <author>Stefan Lüders</author>
    <author>Horacio Lisdero Scaffino</author>
    <author>Michael Höpler</author>
    <author>Felix Böhmer</author>
    <author>Matthias Pfaff</author>
    <author>Dietmar Stephan</author>
    <author>Sabine Kruschwitz</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sustainable building materials</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sequential learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data-driven materials design</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Alkali-activated building materials</value>
    </subject>
    <collection role="ddc" number="620">Ingenieurwissenschaften und zugeordnete Tätigkeiten</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
    <collection role="themenfelder" number="">Material</collection>
    <collection role="literaturgattung" number="">Verlagsliteratur</collection>
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    <collection role="unnumberedseries" number="">Wissenschaftliche Artikel der BAM</collection>
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    <collection role="themenfelder" number="">Materialdesign</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/58437/data_driven_design.pdf</file>
  </doc>
  <doc>
    <id>61662</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>274</pageFirst>
    <pageLast>282</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>2</volume>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
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    <title language="eng">Beyond Theory: Pioneering AI-Driven Materials Design in the Sustainable Building Material Lab</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Proceedings of the RILEM Spring Convention and Conference 2024</parentTitle>
    <identifier type="issn">2211-0852</identifier>
    <identifier type="isbn">978-3-03170281-5</identifier>
    <identifier type="doi">10.1007/978-3-031-70281-5_31</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="eventName">4 RILEM Spring Convention and Conference on  advanced construction materials and processes for a carbon neutral  society 2024</enrichment>
    <enrichment key="eventPlace">Milano, Italy</enrichment>
    <enrichment key="eventStart">07.04.2024</enrichment>
    <enrichment key="eventEnd">12.04.2024</enrichment>
    <enrichment key="date_peer_review">05.05.2025</enrichment>
    <author>Christoph Völker</author>
    <author>Elisabeth John</author>
    <author>Rafia Firdous</author>
    <author>Tamino Hirsch</author>
    <author>Daria Kaczmarek</author>
    <author>Kevin Ziesack</author>
    <author>Anja Buchwald</author>
    <author>Dietmar Stephan</author>
    <author>Sabine Kruschwitz</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Secondary Raw Materials</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data-Driven Design</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sequential Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Design of Experiments</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Alkali-Activated Binder</value>
    </subject>
    <collection role="ddc" number="620">Ingenieurwissenschaften und zugeordnete Tätigkeiten</collection>
    <collection role="institutes" number="">6 Materialchemie</collection>
    <collection role="institutes" number="">6.1 Oberflächen- und Dünnschichtanalyse</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
    <collection role="themenfelder" number="">Material</collection>
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    <collection role="institutes" number="">8.0 Abteilungsleitung und andere</collection>
    <collection role="themenfelder" number="">Materialdesign</collection>
  </doc>
</export-example>
