Artificial intelligence and quantum chemistry unveil next-generation "dual-modulated" catalysts for fuel cells

Shanghai Jiao Tong University Journal Center

Facebook

Email

 

image: 

(a) Modification strategies for Fe–N–C catalyst via in-plane doping and axial coordination, showing 13 candidate elements for substitutional doping or axial coordination; (b) schematic illustration of the workflow used for the stability and ORR activity analysis of Fe–N–C catalysts under doping and axial coordination modifications.

view more 

Credit: Zongxuan Yang, Qingchen Wu, Hongwei Zhang, Cejun Hu, Junjie Ge, Xiaojun Bao & Pei Yuan.

Fuel cells act as highly sustainable energy conversion devices that exhibit tremendous potential for the global transition toward clean energy systems. However, their widespread deployment is currently limited by the sluggish kinetics of the oxygen reduction reaction (ORR) at the cathode. While platinum-based (Pt/C) materials are highly efficient at accelerating the ORR, their high cost and susceptibility to poisoning severely limit large-scale commercial utilization.

To overcome these barriers, a collaborative research team from Fuzhou University, Qingyuan Innovation Laboratory, and the University of Science and Technology of China has published a groundbreaking study in ENGINEERING Energy. The researchers utilized an innovative combination of density functional theory (DFT) and machine learning (ML) to systematically investigate Fe-N-C single-atom catalysts, which serve as earth-abundant, highly promising alternatives to traditional platinum-based catalysts.

Historically, identifying optimal modification strategies to enhance the activity and stability of Fe-N-C catalysts through traditional trial-and-error procedures has been immensely challenging due to the vast combinatorial space of possible heteroatom types and doping sites. By creating 158 modified Fe-N-C catalyst models, the team thoroughly explored a "dual modulation" strategy that incorporates both in-plane heteroatom doping and axial coordination decoration.

This research successfully establishes a unified mechanistic and data-driven framework that will significantly accelerate the design of high-performance electrocatalysts.

Key Research Highlights and Findings:

• Axial ligands significantly outperform in-plane dopants: The study reveals that attaching axial ligands above the Fe-N-C plane has a far more profound influence on ORR performance than substituting atoms within the carbon plane.
• Electronic modulation mechanism: Axial ligands primarily optimize catalyst performance by modulating the Fe dz2 orbital, which extracts electron density and effectively weakens the adsorption of *OH intermediates on the Fe center.
• Machine learning accelerates discovery: By training Random Forest ML models, the team extracted interpretable descriptors. For catalyst stability, the electron affinity and atomic radius of axial heteroatoms emerged as the most critical factors; for ORR activity, the p-electron count and electronegativity of axial ligands played dominant roles.
• Prediction of novel, high-activity catalysts: Using their validated ML models, the researchers successfully screened from 864 designed structures and identified new dual-modified Fe-N-C candidates that exhibit higher ORR activity than the pristine model.
• Fluorine as the ultimate axial ligand: The incorporation of axial F on the Fe center was proven to effectively tune ORR energetics due to its high electronegativity and compact atomic radius. Notably, six novel high-performance candidates (FeNC-O(4)-F, FeNC-N(4)-F, FeNC-P(2)-F, FeNC-P(4)-F, FeNC-S(2)-F, and FeNC-O(2)-F) which share axial fluorine coordination paired with distinct in-plane dopants were successfully identified and validated via DFT calculations.

This synergistic application of computational chemistry and artificial intelligence precisely unravels the complex interactions within dual-modified single-atom catalysts, paving the way for the development of cheaper, more efficient hydrogen fuel cells.

 

Journal: ENGINEERING Energy

Read the full article for free: https://rdcu.be/frN5f

Cite this article: Yang, Z., Wu, Q., Zhang, H. et al. Dual modulation of Fe–N–C catalysts via axial and in-plane heteroatoms for oxygen reduction: A combined DFT and machine learning study.ENG. Energy 20, 10740 (2026). https://doi.org/10.1007/s11708-026-1074-0

---

Journal

ENGINEERING Energy

DOI

10.1007/s11708-026-1074-0 

Method of Research

News article

Article Title

Dual modulation of Fe–N–C catalysts via axial and in-plane heteroatoms for oxygen reduction: A combined DFT and machine learning study