MIT Uses AI to Find Catalysts for Low-Energy Ammonia Production
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MIT researchers have developed an AI-driven method to identify catalyst combinations that could reduce the energy required for electrochemical ammonia production. The approach uses quantum-mechanical calculations and machine learning to predict which alloys can overcome key reaction bottlenecks. Ammonia production currently consumes about 2% of global energy and emits 1.5% of greenhouse gases.
Key Facts
- Ammonia production consumes about 2% of the world's energy and generates roughly 1.5% of global greenhouse gas emissions.
- Roughly 80% of the 200 million metric tons of ammonia produced globally each year goes into nitrogen fertilizers.
- MIT researchers led by Constantine Athanitis used quantum-mechanical calculations and machine learning to predict alloy combinations for electrochemical ammonia synthesis.
- Electrochemical ammonia synthesis replaces fossil-fuel-derived hydrogen and extreme temperatures and pressures with electricity, water, and nitrogen.
The AI-Driven Catalyst Search
Researchers at MIT have developed a method that uses AI and quantum-mechanical calculations to identify catalyst combinations most likely to cut the energy required to produce ammonia electrochemically. Instead of synthesizing and testing thousands of alloys one by one, the researchers use AI to predict which combinations are most likely to lower the energy needed to make ammonia. Led by researcher Constantine Athanitis, the MIT team used quantum-mechanical calculations to determine which microscopic properties control different stages of the reaction, then trained machine-learning models to predict which alloys could overcome bottlenecks including nitrogen dissociation and hydrogen transfer. MIT is focusing on metal nitride catalysts because their own nitrogen can participate in ammonia production, reducing some of the energy needed to break apart nitrogen molecules from the air.
Challenges in Electrochemical Ammonia Synthesis
Electrochemical ammonia synthesis has been studied for decades as an alternative to the traditional Haber-Bosch process. Electrochemical synthesis replaces the fossil-fuel-derived hydrogen and extreme temperatures and pressures used by Haber-Bosch with electricity, water, and nitrogen. If that electricity comes from low-carbon sources, ammonia can be produced without natural gas or coal as a feedstock. Electrochemical ammonia production remains too slow and inefficient to compete with Haber-Bosch at industrial scale, with much of the difficulty caused by nitrogen. The two atoms in a nitrogen molecule are held together by an extremely strong triple bond that requires high levels of energy to break before the nitrogen can react to form ammonia, according to the researchers.
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MIT Uses AI to Find Catalysts for Low-Energy Ammonia Production
