About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Late News Poster Session
|
| Presentation Title |
H-31: Accelerating Discovery of High Entropy Alloy Catalysts for Nitrate Reduction to Ammonia with a Universal Machine Learning Interatomic Potential |
| Author(s) |
Joshua Young, Qing-Jie Li, Taku Watanabe |
| On-Site Speaker (Planned) |
Joshua Young |
| Abstract Scope |
Electrocatalytic nitrate reduction to ammonia offers a sustainable alternative to the energy-intensive Haber-Bosch process, but designing efficient catalysts remains a challenge. High-entropy alloys (HEAs) are promising candidates, but their vast compositional space makes traditional experimental or computational optimization intractable. Here, we use a universal machine learning interatomic potential (PreFerred Potential, PFP) to enable a high-throughput statistical approach to mapping reaction free energy landscapes on complex catalyst surfaces. We screened thousands of surface compositions of HEAs, creating a large dataset of diverse local active site environments. For each, we calculated the Gibbs free energy for every intermediate and key transition state along the NO3RR pathway, constructing free energy diagrams and elucidating complex composition-activity relationships. We integrated these free energies into machine learning models to predict ideal HEA catalyst compositions with optimal NO3⁻ and NH3 binding properties. This PFP-enabled framework can dramatically speed up the discovery of complex catalysts for challenging reactions. |
| Proceedings Inclusion? |
Undecided |
| Keywords |
Energy Conversion and Storage, High-Entropy Alloys, Computational Materials Science & Engineering |