Billions of Options, One Green Catalyst: How AI Supercharged Sustainable Ammonia

AI narrows catalyst discovery for green ammonia

Ammonia may be a quiet hero in global agriculture, but behind its nitrogen-rich benefits lies one of the most carbon-intensive industrial processes on Earth. Traditionally made under high pressure and scorching temperatures via the Haber–Bosch process, ammonia production currently contributes about 2% of global greenhouse gas emissions. But researchers at UNSW Sydney are rewriting that story — with artificial intelligence at the helm.

From Green Chemistry to Greener Engineering

Back in 2021, a team at UNSW developed a method for synthesizing ammonia at room temperature using only air, water, and renewable electricity. But this pioneering concept still needed a performance upgrade — particularly in finding the right catalyst that could maximize reaction rates and energy efficiency.

This is where AI stepped in. The team, led by Dr. Ali Jalili, began with 13 known metals suitable for ammonia catalysis. When combined, they presented over 8,000 possible alloy permutations. Exhaustively testing each of them in the lab was unfeasible. So instead, the researchers trained a machine-learning model to predict which combinations were most promising.

A Needle in a Nano-Haystack

The AI filtered through all 8,000 candidates and narrowed the search down to just 28. Among them, the winning catalyst was a high-entropy alloy composed of iron, bismuth, nickel, tin, and zinc. This five-metal blend delivered astonishing results: a sevenfold improvement in ammonia output rate and nearly 100% Faradaic efficiency — meaning almost every electron used was converted into ammonia.

Better still, this feat was accomplished at just 25°C — less than 10% of the temperature needed for Haber–Bosch. The implications for decarbonizing global fertilizer production are profound.

Decentralized, Scalable, and Future-Proof

The breakthrough goes beyond chemistry — it’s a potential economic disruptor. Instead of large, centralized ammonia factories, the UNSW team is prototyping modular systems the size of shipping containers. These “green ammonia modules” can be deployed directly to farms, reducing transport emissions and decentralizing production.

These systems could also play a dual role in the hydrogen economy. Ammonia is a superior hydrogen carrier compared to liquid hydrogen, making it an ideal medium for renewable energy storage and transportation.

Catalyzing a Carbon-Free Future

This project exemplifies the convergence of AI, electrochemistry, and green engineering. By accelerating the discovery process and optimizing performance at ambient conditions, the team has not only redefined what’s possible for ammonia — they’ve also charted a course for future breakthroughs in sustainable catalysis.

As energy markets shift and the demand for carbon-free production intensifies, innovations like this one will form the backbone of next-generation chemical manufacturing.

🔗 Original article from Phys.org: https://phys.org/news/2025-06-ai-narrow-catalyst-options-supercharges.html

Sponsored by PWmat (Lonxun Quantum) – a leading developer of GPU-accelerated materials simulation software for cutting-edge quantum, energy, and semiconductor research. Learn more about our solutions at: https://www.pwmat.com/en

📞 Phone: +86 400-618-6006
📧 Email: support@pwmat.com

Comments

Post a Comment

Popular posts from this blog

Quantum Chemistry Meets AI: A New Era for Molecular Machine Learning

Image
In a powerful leap forward for computational chemistry and materials design, researchers from Carnegie Mellon University have developed a new kind of molecular machine learning model—one that goes beyond simplistic molecular graphs and integrates fundamental principles of quantum chemistry. Their work, published in Nature Machine Intelligence , could radically enhance our ability to predict chemical behavior, optimize catalysts, and discover new drugs, all while using less data and gaining more scientific insight. The Problem: Traditional Molecular Representations Fall Short Molecular machine learning (ML) models underpin key processes in drug discovery, materials science, and catalysis. However, most existing models use simplified representations such as SMILES strings, 2D graphs, or coordinate matrices. These techniques often lack the quantum-chemical information that governs how molecules truly behave—particularly electron interactions that define reactivity, stability, and...
Read more

MIT’s React-OT: The AI Model Revolutionizing Chemical Reaction Design

Image
MIT’s React-OT: The AI Model Revolutionizing Chemical Reaction Design In a major leap for materials science and computational chemistry, researchers at MIT have unveiled a new AI-driven model called React-OT , capable of predicting the “point of no return” in chemical reactions — also known as the transition state — with remarkable speed and accuracy. Published in Phys.org on April 23, 2025, this innovation could revolutionize how scientists design sustainable chemical processes and novel materials. Why it matters: Understanding and predicting transition states is vital for designing efficient reactions in pharmaceuticals, polymers, and fuels. Traditional methods based on quantum chemistry are computationally expensive — sometimes taking days for a single calculation. Enter React-OT — a machine-learning model trained on over 9,000 reactions that slashes prediction time to under a second. The model’s secret? It begins with a smart est...
Read more

OMol25: A Record-Breaking Dataset Set to Revolutionize AI in Computational Chemistry

Image
Published on: Quantum Server Networks | Date: May 2025 In an era where artificial intelligence is reshaping every scientific frontier, a groundbreaking resource named Open Molecules 2025 (OMol25) has just been launched. This record-breaking dataset is poised to radically transform the way researchers model and simulate molecular interactions, accelerating innovation in materials science, biochemistry, and clean energy. Berkeley Lab News Center reports that the dataset, jointly developed by Meta’s Fundamental AI Research (FAIR) lab and the U.S. Department of Energy’s Lawrence Berkeley National Laboratory, encompasses over 100 million high-fidelity 3D molecular snapshots . These were generated using Density Functional Theory (DFT), a quantum mechanical modeling method renowned for its precision but traditionally limited by massive computational demands. What Makes OMol25 So Important? DFT simulations have long been a gold standard for predicting how atoms behave, includ...
Read more