Machine learning-assisted Ru-N bond regulation for ammonia synthesis

Zichuang Li, Mingxin Zhang, Xiaozhi Su, Yangfan Lu, Jiang Li, Qing Zhang, Wenqian Li, Kailong Qian, Xiaojun Lu, Bo Dai, Hideo Hosono, Yanpeng Qi (), Miao Xu, Renzhong Tai, Jie-Sheng Chen and Tian-Nan Ye ()
Additional contact information
Zichuang Li: Shanghai Jiao Tong University
Mingxin Zhang: Shanghai Tech University
Xiaozhi Su: Chinese Academy of Sciences
Yangfan Lu: Chongqing University
Jiang Li: Tokyo Institute of Technology
Qing Zhang: Shanghai Tech University
Wenqian Li: Shanghai Jiao Tong University
Kailong Qian: Shanghai Jiao Tong University
Xiaojun Lu: Shanghai Jiao Tong University
Bo Dai: Shanghai Jiao Tong University
Hideo Hosono: Tokyo Institute of Technology
Yanpeng Qi: Shanghai Tech University
Miao Xu: Shanghai Institute of Space Power-Sources
Renzhong Tai: Chinese Academy of Sciences
Jie-Sheng Chen: Shanghai Jiao Tong University
Tian-Nan Ye: Shanghai Jiao Tong University

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: Abstract Ruthenium-bearing intermetallics (Ru-IMCs) featured with well-defined structures and variable compositions offer new opportunities to develop ammonia synthesis catalysts under mild conditions. However, their complex phase nature and the numerous controlling parameters pose major challenges for catalyst design and exploration. Herein, we demonstrate that a combination of machine learning (ML) and model mining techniques can effectively address these challenges. Based on the combination techniques, we generate a two-dimensional activity volcano plot with adsorption energies of N2 and N, and identify the radius of atom coordinating to Ru as a key parameter. The well-designed Sc1/8Nd7/8Ru2 reaches as high as 8.18 mmol m−2 h−1 at 0.1 MPa and 400 °C. Density functional theory (DFT) calculations combined with N2-TPD and FT-IR studies reveal that Ru‒N interaction is controlled by the d-p orbital hybridization between Ru and N. These findings underscore the importance of ML towards material design on exploring IMCs for ammonia synthesis.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-63064-3 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-63064-3

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-63064-3

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().