Citation: Yajun Mao, Huchuan Yan, Keteng Li, Cui Lai, Xing Fan, Dengsheng Ma, Guangming Zeng, Lei Qin. Machine learning in electrocatalytic ammonia synthesis: A mini review on catalyst discovery and optimization[J]. Chinese Chemical Letters, ;2026, 37(9): 112440. doi: 10.1016/j.cclet.2026.112440 shu

Machine learning in electrocatalytic ammonia synthesis: A mini review on catalyst discovery and optimization

College of Environmental Science and Engineering and Key Laboratory of Environmental Biology and Pollution Control (Ministry of Education), Hunan University, Changsha 410082, China

leiqin@hnu.edu.cn

Received Date: 26 July 2025
Revised Date: 6 January 2026
Accepted Date: 21 January 2026
Available Online: 22 January 2026

Figures(8)

The availability of extensive catalytic data from experiments and computations has facilitated the utilization of machine learning (ML) in developing electrocatalysts for the electrocatalytic nitrogen reduction reaction (eNRR). ML has emerged as an effective paradigm to accelerate the discovery and optimization of eNRR catalysts, leveraging its capabilities in automated processing, interpretation, and prediction of high-performance candidates. Crucially, ML enabled the construction of precise data-driven models, establishing key structure-property relationships between material characteristics (e.g., electronic structure, surface properties) and target catalytic performance (activity, selectivity, stability). This review systematically summarized recent advances in ML for eNRR. ML and deep learning methodologies are examined, focusing on two critical aspects: high-throughput catalyst screening and electrocatalytic process optimization. A critical assessment is provided on the commonalities and challenges in model development, encompassing dataset construction, optimal model selection, and interpretability. Finally, current limitations of ML in eNRR research are discussed and the future perspectives and opportunities for the field are also outlined.
- Ammonia,
- Electrocatalytic nitrogen reduction reaction,
- Electrocatalyst,
- Machine learning,
- Artificial intelligence
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沈阳化工大学材料科学与工程学院沈阳 110142