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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Amorphous Catalysis: Machine Learning Driven High-Throughput Screening of Superior Active Site for Hydrogen Evolution Reaction
Jiawei Zhang, P. Hu, Haifeng Wang
The Journal of Physical Chemistry C (2020) Vol. 124, Iss. 19, pp. 10483-10494
Closed Access | Times Cited: 54

Showing 1-25 of 54 citing articles:

Structural Transformation of Heterogeneous Materials for Electrocatalytic Oxygen Evolution Reaction
Hui Ding, Hongfei Liu, Wangsheng Chu, et al.
Chemical Reviews (2021) Vol. 121, Iss. 21, pp. 13174-13212
Closed Access | Times Cited: 431
Data‐Driven Materials Innovation and Applications
Zhuo Wang, Zhehao Sun, Hang Yin, et al.
Advanced Materials (2022) Vol. 34, Iss. 36
Closed Access | Times Cited: 108
Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning
Jianwen Liu, Wenzhi Luo, Lei Wang, et al.
Advanced Functional Materials (2022) Vol. 32, Iss. 17
Closed Access | Times Cited: 79
Computational chemistry for water-splitting electrocatalysis
Licheng Miao, Wenqi Jia, Xuejie Cao, et al.
Chemical Society Reviews (2024) Vol. 53, Iss. 6, pp. 2771-2807
Closed Access | Times Cited: 53
Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation
Rui Ding, Junhong Chen, Yuxin Chen, et al.
Chemical Society Reviews (2024)
Open Access | Times Cited: 22
Inhibiting Overoxidation of Dynamically Evolved RuO2 to Achieve a Win–Win in Activity–Stability for Acidic Water Electrolysis
Wenjing Li, Dingming Chen, Zhenxin Lou, et al.
Journal of the American Chemical Society (2025)
Closed Access | Times Cited: 2
Machine learning in solid heterogeneous catalysis: Recent developments, challenges and perspectives
Yani Guan, Donovan Chaffart, Guihua Liu, et al.
Chemical Engineering Science (2021) Vol. 248, pp. 117224-117224
Closed Access | Times Cited: 88
Machine Learning for Transition-Metal-Based Hydrogen Generation Electrocatalysts
Min Wang, Hongwei Zhu
ACS Catalysis (2021) Vol. 11, Iss. 7, pp. 3930-3937
Closed Access | Times Cited: 72
Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis
Jiayan Xu, Xiaoming Cao, P. Hu
Physical Chemistry Chemical Physics (2021) Vol. 23, Iss. 19, pp. 11155-11179
Closed Access | Times Cited: 61
Machine learning accelerated calculation and design of electrocatalysts for CO2 reduction
Zhehao Sun, Hang Yin, Kaili Liu, et al.
SmartMat (2022) Vol. 3, Iss. 1, pp. 68-83
Open Access | Times Cited: 50
Data-driven design of electrocatalysts: principle, progress, and perspective
Shan Zhu, Kezhu Jiang, Biao Chen, et al.
Journal of Materials Chemistry A (2023) Vol. 11, Iss. 8, pp. 3849-3870
Closed Access | Times Cited: 23
Rational Design of Graphene Derivatives for Electrochemical Reduction of Nitrogen to Ammonia
Mandira Majumder, Haneesh Saini, Ivan Dědek, et al.
ACS Nano (2021) Vol. 15, Iss. 11, pp. 17275-17298
Closed Access | Times Cited: 56
Machine-learning accelerated geometry optimization in molecular simulation
Yilin Yang, Omar A. Jiménez-Negrón, John R. Kitchin
The Journal of Chemical Physics (2021) Vol. 154, Iss. 23
Open Access | Times Cited: 55
Application of density functional theory and machine learning in heterogenous-based catalytic reactions for hydrogen production
Lord Ugwu, Yasser Morgan, Hussameldin Ibrahim
International Journal of Hydrogen Energy (2021) Vol. 47, Iss. 4, pp. 2245-2267
Closed Access | Times Cited: 47
Machine learning utilized for the development of proton exchange membrane electrolyzers
Rui Ding, Yawen Chen, Zhiyan Rui, et al.
Journal of Power Sources (2022) Vol. 556, pp. 232389-232389
Closed Access | Times Cited: 29
Complimentary Computational Cues for Water Electrocatalysis: A DFT and ML Perspective
Ahmed Badreldin, O. Bouhali, Ahmed Abdel‐Wahab
Advanced Functional Materials (2023) Vol. 34, Iss. 12
Closed Access | Times Cited: 21
Toward Next-Generation Heterogeneous Catalysts: Empowering Surface Reactivity Prediction with Machine Learning
Xinyan Liu, Hong‐Jie Peng
Engineering (2024) Vol. 39, pp. 25-44
Open Access | Times Cited: 7
Building a Library for Catalysts Research Using High‐Throughput Approaches
Xiaorui Liu, Bin Liu, Jia Ding, et al.
Advanced Functional Materials (2021) Vol. 32, Iss. 1
Closed Access | Times Cited: 31
Structural and Composition Evolution of Palladium Catalyst for CO Oxidation under Steady-State Reaction Conditions
Jiawei Wu, Dingming Chen, Jianfu Chen, et al.
The Journal of Physical Chemistry C (2023) Vol. 127, Iss. 13, pp. 6262-6270
Closed Access | Times Cited: 12
Saccharide concentration prediction from proxy sea surface microlayer samples analyzed via infrared spectroscopy and quantitative machine learning
Abigail Enders, Nicole North, Jessica B. Clark, et al.
(2024)
Open Access | Times Cited: 4
Theoretical Calculation Assisted by Machine Learning Accelerate Optimal Electrocatalyst Finding for Hydrogen Evolution Reaction
Yue‐Fei Zhang, Xuefei Liu, Wentao Wang
ChemElectroChem (2024) Vol. 11, Iss. 13
Open Access | Times Cited: 4
Computational Data-Driven Materials Discovery
Arun Mannodi‐Kanakkithodi, Maria K. Y. Chan
Trends in Chemistry (2021) Vol. 3, Iss. 2, pp. 79-82
Open Access | Times Cited: 24
Machine learning aided synthesis and screening of HER catalyst: Present developments and prospects
M Karthikeyan, Durga Madhab Mahapatra, Abdul Syukor Abd Razak, et al.
Catalysis Reviews (2022), pp. 1-31
Closed Access | Times Cited: 18
Applications of machine learning in surfaces and interfaces
Shaofeng Xu, Jing‐Yuan Wu, Ying Guo, et al.
Chemical Physics Reviews (2025) Vol. 6, Iss. 1
Open Access
Deep learning potential-assisted surface engineering for HfO2/SiO2 interface and enhanced laser damage resistance
Yongnian Qi, Xiaoguang Guo, Xing Gao, et al.
Applied Surface Science (2025), pp. 163540-163540
Closed Access
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