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  Scopus ID: 21100926589

Presenting SLAMD – A Sequential Learning Based Software for the Inverse Design of Sustainable Cementitious Materials

Christoph Völker, Benjami Moreno Torres, Ghezal Ahmad Zia, Rafia Firdous, Tehseen Rug, Felix Böhmer, Dietmar Stephan and Sabine Kruschwitz

Abstract

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.

Published on: September 28, 2023
doi: 10.17756/nwj.2023-s2-032
Citation: Volker C, Torres BM, Zia GA, Firdous R, Rug T, et al. 2023. Presenting SLAMD – A Sequential Learning Based Software for the Inverse Design of Sustainable Cementitious Materials. NanoWorld J 9(S2): S180-S187.

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