Sequential learning to accelerate discovery of alkali-activated binders

dc.contributor.authorVölker, Christoph
dc.contributor.authorFirdous, Rafia
dc.contributor.authorStephan, Dietmar
dc.contributor.authorKruschwitz, Sabine
dc.date.accessioned2023-04-11T06:36:28Z
dc.date.available2023-04-11T06:36:28Z
dc.date.issued2021-07-19
dc.date.updated2023-03-25T08:47:28Z
dc.description.abstractAlkali-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.en
dc.identifier.eissn1573-4803
dc.identifier.issn0022-2461
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/18518
dc.identifier.urihttps://doi.org/10.14279/depositonce-17327
dc.language.isoen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc600 Technik, Medizin, angewandte Wissenschaften::670 Industrielle Fertigung::670 Industrielle Fertigung
dc.subject.othermaterials scienceen
dc.subject.othercharacterization and evaluation of materialsen
dc.subject.otherpolymer sciencesen
dc.subject.othersolid mechanicsen
dc.subject.othercrystallographyen
dc.subject.otherscattering methodsen
dc.titleSequential learning to accelerate discovery of alkali-activated bindersen
dc.typeArticle
dc.type.versionpublishedVersion
dcterms.bibliographicCitation.doi10.1007/s10853-021-06324-z
dcterms.bibliographicCitation.issue28
dcterms.bibliographicCitation.journaltitleJournal of Materials Science
dcterms.bibliographicCitation.originalpublishernameSpringer Nature
dcterms.bibliographicCitation.originalpublisherplaceHeidelberg
dcterms.bibliographicCitation.pageend15881
dcterms.bibliographicCitation.pagestart15859
dcterms.bibliographicCitation.volume56
dcterms.rightsHolder.referenceCreative-Commons-Lizenz
tub.accessrights.dnbfree
tub.affiliationFak. 6 Planen Bauen Umwelt::Inst. Bauingenieurwesen::FG Baustoffe und Bauchemie
tub.publisher.universityorinstitutionTechnische Universität Berlin

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