Artikel: Sequential learning to accelerate discovery of alkali-activated binders
Journal of materials science. 2021 Oct., v. 56, no. 28
2021
Abstract: 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 ... ...
Abstract | 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. |
---|---|
Schlagwörter | algorithms ; carbon dioxide ; cement ; compression strength ; data collection |
Sprache | Englisch |
Erscheinungsverlauf | 2021-10 |
Umfang | p. 15859-15881. |
Erscheinungsort | Springer US |
Dokumenttyp | Artikel |
ZDB-ID | 2015305-3 |
ISSN | 1573-4803 ; 0022-2461 |
ISSN (online) | 1573-4803 |
ISSN | 0022-2461 |
DOI | 10.1007/s10853-021-06324-z |
Datenquelle | NAL Katalog (AGRICOLA) |
Volltext online
Zusatzmaterialien
Kategorien
Über subito bestellen
Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.