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 identifying multiple high-performance materials. This result showcases AI's capability to handle complex designs with limited data, marking a significant improvement over conventional methods and demonstrating AI's revolutionary role in sustainable material design.

Our study provides in-depth insights into the real-world application of data-driven design in a laboratory setting, highlighting the effective collaboration between AI-guided design and expert oversight. By showcasing the successful integration of AI, this research contributes to advancing sustainable materials science. It sets the stage for shorter time-to-market development boosting the impact of sustainable building in the construction industry.