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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:

An Adaptive Machine Learning Strategy for Accelerating Discovery of Perovskite Electrocatalysts
Zheng Li, Luke E. K. Achenie, Hongliang Xin
ACS Catalysis (2020) Vol. 10, Iss. 7, pp. 4377-4384
Closed Access | Times Cited: 118

Showing 1-25 of 118 citing articles:

The Sabatier Principle in Electrocatalysis: Basics, Limitations, and Extensions
Hideshi Ooka, Jun Huang, Kai S. Exner
Frontiers in Energy Research (2021) Vol. 9
Open Access | Times Cited: 322
Machine learning for perovskite materials design and discovery
Qiuling Tao, Pengcheng Xu, Minjie Li, et al.
npj Computational Materials (2021) Vol. 7, Iss. 1
Open Access | Times Cited: 320
Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery
Haoxin Mai, Tu C. Le, Dehong Chen, et al.
Chemical Reviews (2022) Vol. 122, Iss. 16, pp. 13478-13515
Closed Access | Times Cited: 277
Artificial Intelligence in Chemistry: Current Trends and Future Directions
Zachary J. Baum, Xiang Yu, Philippe Y. Ayala, et al.
Journal of Chemical Information and Modeling (2021) Vol. 61, Iss. 7, pp. 3197-3212
Closed Access | Times Cited: 182
Machine learned features from density of states for accurate adsorption energy prediction
Victor Fung, Guoxiang Hu, Panchapakesan Ganesh, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 174
Machine learning for advanced energy materials
Liu Yun, Oladapo Christopher Esan, Zhefei Pan, et al.
Energy and AI (2021) Vol. 3, pp. 100049-100049
Open Access | Times Cited: 153
Bridging the complexity gap in computational heterogeneous catalysis with machine learning
Tianyou Mou, Hemanth Somarajan Pillai, Siwen Wang, et al.
Nature Catalysis (2023) Vol. 6, Iss. 2, pp. 122-136
Closed Access | Times Cited: 138
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
Design strategies of perovskite nanofibers electrocatalysts for water splitting: A mini review
Yaobin Wang, Yan Jiang, Yunxia Zhao, et al.
Chemical Engineering Journal (2022) Vol. 451, pp. 138710-138710
Closed Access | Times Cited: 98
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
Machine learning for design principles for single atom catalysts towards electrochemical reactions
Mohsen Tamtaji, Hanyu Gao, Md Delowar Hossain, et al.
Journal of Materials Chemistry A (2022) Vol. 10, Iss. 29, pp. 15309-15331
Open Access | Times Cited: 70
Interpretable design of Ir-free trimetallic electrocatalysts for ammonia oxidation with graph neural networks
Hemanth Somarajan Pillai, Yi Li, Shih‐Han Wang, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 61
Machine Learning Descriptors for Data‐Driven Catalysis Study
Li‐Hui Mou, TianTian Han, Pieter E. S. Smith, et al.
Advanced Science (2023) Vol. 10, Iss. 22
Open Access | Times Cited: 53
Active learning guides discovery of a champion four-metal perovskite oxide for oxygen evolution electrocatalysis
Junseok Moon, Wiktor Beker, Marta Siek, et al.
Nature Materials (2023) Vol. 23, Iss. 1, pp. 108-115
Closed Access | Times Cited: 53
Emerging Photoreforming Process to Hydrogen Production: A Future Energy
Sandip Prabhakar Shelake, Dattatray Namdev Sutar, B. Moses Abraham, et al.
Advanced Functional Materials (2024) Vol. 34, Iss. 40
Closed Access | Times Cited: 29
Design Principles and Mechanistic Understandings of Non-Noble-Metal Bifunctional Electrocatalysts for Zinc–Air Batteries
Yunnan Gao, Ling Liu, Yi Jiang, et al.
Nano-Micro Letters (2024) Vol. 16, Iss. 1
Open Access | Times Cited: 27
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
Predicting the Activity and Selectivity of Bimetallic Metal Catalysts for Ethanol Reforming using Machine Learning
Nongnuch Artrith, Zhexi Lin, Jingguang G. Chen
ACS Catalysis (2020) Vol. 10, Iss. 16, pp. 9438-9444
Open Access | Times Cited: 94
Machine learning for halide perovskite materials
Lei Zhang, Mu He, Shaofeng Shao
Nano Energy (2020) Vol. 78, pp. 105380-105380
Closed Access | Times Cited: 93
Infusing theory into deep learning for interpretable reactivity prediction
Shih‐Han Wang, Hemanth Somarajan Pillai, Siwen Wang, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 87
Applications of machine learning in perovskite materials
Ziman Wang, Ming Yang, Xixi Xie, et al.
Advanced Composites and Hybrid Materials (2022) Vol. 5, Iss. 4, pp. 2700-2720
Closed Access | Times Cited: 68
Application of machine learning for advanced material prediction and design
Cheuk Hei Chan, Mingzi Sun, Bolong Huang
EcoMat (2022) Vol. 4, Iss. 4
Open Access | Times Cited: 65
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
Layered Pd oxide on PdSn nanowires for boosting direct H2O2 synthesis
Hongchao Li, Qiang Wan, Congcong Du, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 49
The critical role of A, B-site cations and oxygen vacancies on the OER electrocatalytic performances of Bi0.15Sr0.85Co1−Fe O3−δ (0.2 ≤ x ≤ 1) perovskites in alkaline media
Jing Li, Fan Yang, Yunzhu Du, et al.
Chemical Engineering Journal (2022) Vol. 451, pp. 138646-138646
Closed Access | Times Cited: 48
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