Climate-Friendly Cement Smarter Cement: PSI Uses Machine Learning to Cut CO2 Emissions

The cement industry produces around eight percent of global CO₂ emissions — more than the entire aviation sector worldwide. Researchers at the Paul Scherrer Institute PSI have developed an AI-based model that helps to accelerate the discovery of new cement formulations that could yield the same material quality with a better carbon footprint.

The rotary kilns in cement plants are heated to a scorching 1,400 °C to burn ground limestone down to clinker, the raw material for ready-to-use cement. Unsurprisingly, such temperatures typically can’t be achieved with electricity alone. They are the result of energy-intensive combustion processes that emit large amounts of carbon dioxide (CO₂). What may be surprising, however, is that the combustion process accounts for less than half of these emissions, far less. The majority is contained in the raw materials needed to produce clinker and cement: CO₂ that is chemically bound in the limestone is released during its transformation in the high-temperature kilns.

One promising strategy for reducing emissions is to modify the cement recipe itself — replacing some of the clinker with alternative cementitious materials. That is exactly what an interdisciplinary team in the Laboratory for Waste Management in PSI’s Center for Nuclear Engineering and Sciences has been investigating. Instead of relying solely on time-consuming experiments or complex simulations, the researchers developed a modelling approach based on machine learning. “This allows us to simulate and optimise cement formulations so that they emit significantly less CO₂ while maintaining the same high level of mechanical performance,” explains mathematician Romana Boiger, first author of the study. “Instead of testing thousands of variations in the lab, we can use our model to generate practical recipe suggestions within seconds — it's like having a digital cookbook for climate-friendly cement.”

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With their novel approach, the researchers were able to selectively filter out those cement formulations that could meet the desired criteria. “The range of possibilities for the material composition — which ultimately determines the final properties — is extraordinarily vast,” says Nikolaos Prasianakis head of the Transport Mechanisms Research Group at PSI, who was the initiator and co-author of the study. “Our method allows us to significantly accelerate the development cycle by selecting promising candidates for further experimental investigation.” The results of the study were published in the journal Materials and Structures.

The right recipe

Already today, industrial by-products such as slag from iron production and fly ash from coal-fired power plants are already being used to partially replace clinker in cement formulations and thus reduce CO₂ emissions. However, the global demand for cement is so enormous that these materials alone cannot meet the need. “What we need is the right combination of materials that are available in large quantities and from which high-quality, reliable cement can be produced,” says John Provis, head of the Cement Systems Research Group at PSI and co-author of the study.

Finding such combinations, however, is challenging: “Cement is basically a mineral binding agent — in concrete, we use cement, water, and gravel to artificially create minerals that hold the entire material together,” Provis explains. “You could say we're doing geology in fast motion.” This geology — or rather, the set of physical processes behind it — is enormously complex, and modelling it on a computer is correspondingly computationally intensive and expensive. That is why the research team is relying on artificial intelligence.

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AI as computational accelerator

Artificial neural networks are computer models that are trained, using existing data, to speed up complex calculations. During training, the network is fed a known data set and learns from it by adjusting the relative strength or “weighting” of its internal connections so that it can quickly and reliably predict similar relationships. This weighting serves as a kind of shortcut — a faster alternative to otherwise computationally intensive physical modelling.

The researchers at PSI also made use of such a neural network. They themselves generated the data required for training: “With the help of the open-source thermodynamic modelling software Gems, developed at PSI, we calculated — for various cement formulations — which minerals form during hardening and which geochemical processes take place,” explains Nikolaos Prasianakis. By combining these results with experimental data and mechanical models, the researchers were able to derive a reliable indicator for mechanical properties — and thus for the material quality of the cement. For each component used, they also applied a corresponding CO₂ factor, a specific emission value that made it possible to determine the total CO₂ emissions. “That was a very complex and computationally intensive modelling exercise,” the scientist says.

But it was worth the effort — with the data generated in this way, the AI model was able to learn. “Instead of seconds or minutes, the trained neural network can now calculate mechanical properties for an arbitrary cement recipe in milliseconds — that is, around a thousand times faster than with traditional modelling,” Boiger explains.

From output to input

How can this AI now be used to find optimal cement formulations — with the lowest possible CO₂ emissions and high material quality? One possibility would be to try out various formulations, use the AI model to calculate their properties, and then select the best variants. A more efficient approach, however, is to reverse the process. Instead of trying out all options, ask the question the other way around: Which cement composition meets the desired specifications regarding CO₂ balance and material quality?

Both the mechanical properties and the CO₂ emissions depend directly on the recipe. “Viewed mathematically, both variables are functions of the composition — if this changes, the respective properties also change,” the mathematician explains. To determine an optimal recipe, the researchers formulate the problem as a mathematical optimisation task: They are looking for a composition that simultaneously maximises mechanical properties and minimises CO₂ emissions. “Basically, we are looking for a maximum and a minimum — from this we can directly deduce the desired formulation,” the mathematician says.

To find the solution, the team integrated in the workflow an additional AI technology, the so-called genetic algorithms — computer-assisted methods inspired by natural selection. This enabled them to selectively identify formulations that ideally combine the two target variables.

The advantage of this “reverse approach”: You no longer have to blindly test countless recipes and then evaluate their resulting properties; instead you can specifically search for those that meet specific desired criteria — in this case, maximum mechanical properties with minimum CO₂ emissions.

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Interdisciplinary approach with great potential

Among the cement formulations identified by the researchers, there are already some promising candidates. “Some of these formulations have real potential,” says John Provis, “not only in terms of CO₂ reduction and quality, but also in terms of practical feasibility in production.” To complete the development cycle, however, the recipes must first be tested in the laboratory. “We're not going to build a tower with them right away without testing them first,” Nikolaos Prasianakis says with a smile.

The study primarily serves as a proof of concept — that is, as evidence that promising formulations can be identified purely by mathematical calculation. “We can extend our AI modelling tool as required and integrate additional aspects, such as the production or availability of raw materials, or where the building material is to be used — for example, in a marine environment, where cement and concrete behave differently, or even in the desert,” says Romana Boiger and Nikolaos Prasianakis is already looking ahead: “This is just the beginning. The time savings offered by such a general workflow are enormous — making it a very promising approach for all sorts of material and system designs.”

Without the interdisciplinary background of the researchers, the project would never have come to fruition: “We needed cement chemists, thermodynamics experts, AI specialists — and a team that could bring all of this together,” Prasianakis says. “Added to this was the important exchange with other research institutions such as EMPA within the framework of the ’Scene’ project.” Scene (the Swiss Centre of Excellence on Net Zero Emissions) is an interdisciplinary research programme that aims to develop scientifically sound solutions for drastically reducing greenhouse gas emissions in industry and the energy supply. The study was carried out as part of this project.

Original Article: Machine learning-accelerated discovery of green cement recipes; Materials and Structures; DOI:10.1617/s11527-025-02684-z

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