Market and product

Compiled by Bao Hien

Researchers at the Massachusetts Institute of Technology (MIT) have developed a new computational approach to predict which materials hold the most promise as catalysts for electrochemical ammonia production — a pathway that could eventually replace part of the traditional Haber-Bosch process, which remains heavily dependent on fossil fuels.

Why a New Ammonia Production Process Is Needed

Ammonia is one of the most important chemicals in the world, ranking second only to sulfuric acid in total volume produced each year, with roughly 200 million metric tons used annually — most of it to make the fertilizer that feeds the global population. Yet ammonia production currently accounts for up to 2% of global energy consumption and about 1.5% of global greenhouse gas emissions.

More than 90% of ammonia today is still made using the Haber-Bosch process — a method developed more than a century ago that combines nitrogen and hydrogen under high heat and pressure, with both the heat and the hydrogen feedstock coming largely from fossil fuels. According to Constantine Athanitis, a doctoral student in MIT's Department of Materials Science and Engineering (DMSE), the process has been "hyper-optimized" since it was first introduced, making further improvements increasingly difficult.

One alternative is to use electrochemistry instead of heat and pressure to drive the reaction — essentially an electrochemical reaction between proton-electron pairs and nitrogen gas, using the same basic principles as electrolyzers. However, according to Athanitis, this method has so far fallen short of the production rates and efficiency needed to be cost-competitive at industrial scale — and in a market economy, a technology that's better for the climate still won't gain traction unless it can compete on cost.

The Approach: Density Functional Theory and Machine Learning Instead of Trial and Error

The key component determining the efficiency of the electrochemical process is the metallic catalyst material, whose properties directly govern the reaction occurring on its surface. The traditional approach to materials research relies largely on trial and error: taking an existing material and tweaking it based on scientific intuition — a process that can take years given the millions of possible alloy combinations.

The research team, led by Bilge Yildiz, the Breen M. Kerr Professor in MIT's Departments of Nuclear Science and Engineering and Materials Science and Engineering, took a different approach: first identifying the microscopic properties that determine a material's ability to drive the nitrogen reduction reaction, rather than searching randomly through every possible alloy combination. The team used density functional theory — a method that applies quantum mechanics to simulate material properties and behavior, allowing researchers to predict how different atomic arrangements might perform before making them in the lab — combined with machine learning to identify bottlenecks in the reaction pathway and determine which alloys might be able to overcome them.

The team focused on transition metal nitrides, a class of materials already shown to be effective in electrochemical nitrogen reduction reactions. According to Athanitis, these materials are especially effective because the nitrogen already present in the catalyst itself becomes part of the reaction — producing a series of chemical steps in which one step supplies part of the energy needed to drive the next, reducing the overall energy input required. This helps address one of the biggest bottlenecks in the nitrogen reduction reaction: the very high energy needed to break the strong bonds within nitrogen molecules. Even so, the process remains limited by certain other steps along the reaction pathway, including nitrogen dissociation and hydrogen transfer.

Still at the Theoretical Stage

The findings, published August 11, 2026, in the open-access journal EES Catalysis (Royal Society of Chemistry), remain purely theoretical for now: the team has used computer models to identify promising alloys, but those materials still need to be synthesized and tested experimentally. Dane Morgan, a professor of engineering at the University of Wisconsin who was not involved in the study, called it "exciting work" that helps clarify how a material's fundamental electronic properties relate to its role as a catalyst in ammonia production — insight that can guide the design of new catalysts, both through better qualitative understanding and by accelerating computational screening. However, he also noted that translating these calculations into practical catalysts will require many additional steps, so meaningful real-world impact is likely still some distance away.

The team's next step is to build a working reaction cell — a laboratory device that uses the catalyst to produce ammonia and tests its performance under real operating conditions. According to Athanitis, that step is essential for the research to make a real impact on society.