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Journal of Materials Informatics

C O NT E NT S

Topic: Theory-Assisted Developing Electrocatalysis

1 Regulating the electrocatalytic performance for nitrogen reduction reaction by tuning the N contents in
Fe @N C 20-x (x = 0~4): a DFT exploration
3
x
Bing Han, Fengyu Li
J Mater Inf 2023;3:24 http://dx.doi.org/10.20517/jmi.2023.32

2 Computational design of spatially confined triatomic catalysts for nitrogen reduction reaction
Wei Pei, Wenya Zhang, Xueke Yu, Lei Hou, Weizhi Xia, Zi Wang, Yongfeng Liu, Si Zhou, Yusong Tu, Jijun Zhao
J Mater Inf 2023;3:26 http://dx.doi.org/10.20517/jmi.2023.35

3 High hydrogen evolution activities of dual-metal atoms incorporated N-doped graphenes achieved by
coordination regulation
Cunjin Zhang, Shuaibo Qin, Hui Gao, Peng Jin
J Mater Inf 2024;4:1 http://dx.doi.org/10.20517/jmi.2023.34

4 Local environment interaction-based machine learning framework for predicting molecular adsorption
energy
Yifan Li, Yihan Wu, Yuhang Han, Qiujie Lyu, Hao Wu, Xiuying Zhang, Lei Shen
J Mater Inf 2024;4:4 http://dx.doi.org/10.20517/jmi.2023.41

5 Superior single-atom and single-cluster catalysts towards electrocatalytic nitrogen reduction reactions: a
theoretical perspective
Haihong Meng, Yinghe Zhao, Fengyu Li, Zhongfang Chen
J Mater Inf 2025;5:3 http://dx.doi.org/10.20517/jmi.2024.74

6 Design of Fe Mo@γ-GDY triatomic catalyst for electrocatalytic urea synthesis of N and CO: a theoretical
2
2
study
Linyuan Chi, Tonghui Wang, Qing Jiang
J Mater Inf 2025;5:11 http://dx.doi.org/10.20517/jmi.2024.49

7 Interpretable physics-informed machine learning approaches to accelerate electrocatalyst development
Hao Wu, Mingxuan Chen, Hao Cheng, Tong Yang, Minggang Zeng, Ming Yang
J Mater Inf 2025;5:15 http://dx.doi.org/10.20517/jmi.2024.67