Open Access

Diese Arbeit untersucht den Einsatz von Künstlicher Intelligenz (KI) in der Materialentwicklung für Beton, mit einem Fokus auf Karbonatisierungswiderstand, CO₂-Reduktion und Kostenoptimierung. Das KI-Tool SLAMD, das auf inverses Materialdesign setzt, konnte in Studien die Entwicklungszeit für Betonmischungen um bis zu 80 % verkürzen. Durch die Integration zusätzlicher 1H-Kernspinresonanz-Daten wurde der Prozess um weitere 40 % beschleunigt. Insgesamt zeigt sich, dass der Einsatz von KI die Effizienz und Nachhaltigkeit in der Materialentwicklung signifikant steigern kann.

This study explores the acceleration of material design in the concrete industry, focusing on improving carbonation resistance, a key factor in the durability of concrete structures. Traditional tests for carbonation resistance are lengthy, but with the construction industry aiming for sustainable production, finding a balance between carbonation resistance and CO2 footprint is crucial. Our research employs two innovative methods: 1. Applying the Sequential Learning App for Materials Discovery (SLAMD), an AI materials design framework, to an extensive dataset of real-world concrete compositions to selectively test materials that meet market demands: maximum durability, optimal eco-durability, and the best cost-durability trade-off. 2. Investigating 1H Nuclear Magnetic Resonance (NMR) relaxometry as a quick alternative for characterizing carbonation behavior, as it saves time compared to traditional tests and assesses the complete material’s pore space. Specific NMR features are then integrated into the material design model, with the model’s performance compared against traditional approaches. The results of our study are compelling, demonstrating that materials can be precisely tailored to meet specific requirements with minimal data points. This marks a significant stride in the concrete industry, indicating thatNMR-based, lowfidelity surrogate characterizations, combined with a focused, data-driven design approach, can substantially accelerate the development of durable, sustainable concrete mixtures.

In this paper, we demonstrate the value of 1 H NMR relaxometry for studying the hydration of clinker-reduced, climate-friendly cementitious binders. Our study includes white cement (WC), ordinary Portland cement (OPC), and samples incorporating reactive agro-waste based ashes and non-reactive biochars as supplementary cementitous materials (SCM). NMR measurements were performed over a period of up to 120 h during hydration with an echo time of 50 μ s and a relatively large sample size of 20 mL. The results were compared to heat flow calorimetry (HFC) data, and a detailed comparison with literature data was performed for pure OPC and WC. The results show that time-resolved NMR measurements, especially the analysis of individual NMR signal components assigned to defined 1 H reservoirs, are effective for studying hydration processes. They offer insights into the evolution of the microstrucure and specific chemical phases. NMR provides valuable information and serves as a good complement to HFC. The comparison with data obtained with shorter echo times (40 μs or around 15–45 μs with solid echo sequence) on much smaller samples showed almost identical developments with respect to the T2 distributions. For the SCM samples, NMR results indicated partially accelerated hydration processes compared to classical OPC hydration. One SCM sample acted as a highly reactive pozzolan, showing a similar hydration process to OPC with the strongest effect observed when superplasticizer was added. Adding biochar delayed C-S-H gel pore formation but significantly increased capillary pores and even free water, likely due to the sponge-like structure.

In this paper, we demonstrate the value of 1H NMR relaxometry for studying the hydration of clinker-reduced, more climate-friendly cementitious binders. The results were obtained on typical CEM I cements and sister samples containing two different reactive agricultural ashes as well as non-reactive biochars as supplementary cementitious materials. The findings prove that time-resolved NMR measurements provide valuable additional information when combined with classical heat flow calorimetry.

This article describes advancements in the ongoing digital transformation in materials science and engineering. It is driven by domain‐specific successes and the development of specialized digital data spaces. There is an evident and increasing need for standardization across various subdomains to support science data exchange across entities. The MaterialDigital Initiative, funded by the German Federal Ministry of Education and Research, takes on a key role in this context, fostering collaborative efforts to establish a unified materials data space. The implementation of digital workflows and Semantic Web technologies, such as ontologies and knowledge graphs, facilitates the semantic integration of heterogeneous data and tools at multiple scales. Central to this effort is the prototyping of a knowledge graph that employs application ontologies tailored to specific data domains, thereby enhancing semantic interoperability. The collaborative approach of the Initiative's community provides significant support infrastructure for understanding and implementing standardized data structures, enhancing the efficiency of data‐driven processes in materials development and discovery. Insights and methodologies developed via the MaterialDigital Initiative emphasize the transformative potential of ontology‐based approaches in materials science, paving the way toward simplified integration into a unified, consolidated data space of high value.