With demand for cement alternatives rising, an MIT team uses machine learning to hunt for new ingredients across the scientific literature.
With demand for cement alternatives rising, an MIT team uses machine learning to hunt for new ingredients across the scientific literature.
For weeks, the whiteboard in the lab was crowded with scribbles, diagrams, and chemical formulas. A research team across the Olivetti Group and the MIT Concrete Sustainability Hub (CSHub) was working intensely on a key problem: How can we reduce the amount of cement in concrete to save on costs and emissions?
The question was certainly not new; materials like fly ash, a byproduct of coal production, and slag, a byproduct of steelmaking, have long been used to replace some of the cement in concrete mixes. However, the demand for these products is outpacing supply as industry looks to reduce its climate impacts by expanding their use, making the search for alternatives urgent. The challenge that the team discovered wasn’t a lack of candidates; the problem was that there were too many to sort through.
On May 17, the team, led by postdoc Soroush Mahjoubi, published an open-access paper in Nature’s Communications Materials outlining their solution. “We realized that AI was the key to moving forward,” notes Mahjoubi. “There is so much data out there on potential materials — hundreds of thousands of pages of scientific literature. Sorting through them would have taken many lifetimes of work, by which time more materials would have been discovered!”
Read more at Massachusetts Institute of Technology
Image: Caption:A team led by Soroush Mahjoubi, a postdoc in civil and environmental engineering, built a machine-learning framework that evaluates and sorts candidate materials for cleaner concrete based on their physical and chemical properties. “Some of the most interesting materials that could replace a portion of cement are ceramics,” notes Mahjoubi. “Old tiles, bricks, pottery — all these materials may have high reactivity.” (Credit: Photo: Andrew Laurent via Massachusetts Institute of Technology)
