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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:

Predicting aggregation energy for single atom bimetallic catalysts on clean and O* adsorbed surfaces through machine learning models
Zhuole Lu, Shwetank Yadav, Chandra Veer Singh
Catalysis Science & Technology (2019) Vol. 10, Iss. 1, pp. 86-98
Closed Access | Times Cited: 40

Showing 1-25 of 40 citing articles:

Single-Atom Alloy Catalysis
Ryan T. Hannagan, Georgios Giannakakis, Maria Flytzani‐Stephanopoulos, et al.
Chemical Reviews (2020) Vol. 120, Iss. 21, pp. 12044-12088
Open Access | Times Cited: 788
Directing reaction pathways via in situ control of active site geometries in PdAu single-atom alloy catalysts
Mengyao Ouyang, Konstantinos G. Papanikolaou, Alexey Boubnov, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 133
Applications of Machine Learning in Alloy Catalysts: Rational Selection and Future Development of Descriptors
Ze Yang, Wang Gao
Advanced Science (2022) Vol. 9, Iss. 12
Open Access | Times Cited: 76
Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis
Pushkar Ghanekar, Siddharth Deshpande, Jeffrey Greeley
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 73
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
Bimetallic Single-Atom Catalysts for Water Splitting
Megha A. Deshmukh, Aristides Bakandritsos, Radek Zbořil
Nano-Micro Letters (2024) Vol. 17, Iss. 1
Open Access | Times Cited: 23
From Single Metals to High‐Entropy Alloys: How Machine Learning Accelerates the Development of Metal Electrocatalysts
Xinyu Fan, Letian Chen, Dulin Huang, et al.
Advanced Functional Materials (2024) Vol. 34, Iss. 34
Closed Access | Times Cited: 19
Neural Network-Assisted Development of High-Entropy Alloy Catalysts: Decoupling Ligand and Coordination Effects
Zhuole Lu, Zhiwen Chen, Chandra Veer Singh
Matter (2020) Vol. 3, Iss. 4, pp. 1318-1333
Open Access | Times Cited: 122
Deep dive into machine learning density functional theory for materials science and chemistry
Lenz Fiedler, Karan Shah, Michael Bußmann, et al.
Physical Review Materials (2022) Vol. 6, Iss. 4
Open Access | Times Cited: 69
Rational Design of Atomically Dispersed Metal Site Electrocatalysts for Oxygen Reduction Reaction
Kechuang Wan, Tiankuo Chu, Bing Li, et al.
Advanced Science (2023) Vol. 10, Iss. 11
Open Access | Times Cited: 34
A Surrogate Machine Learning Model for the Design of Single-Atom Catalyst on Carbon and Porphyrin Supports towards Electrochemistry
Mohsen Tamtaji, Shuguang Chen, Ziyang Hu, et al.
The Journal of Physical Chemistry C (2023) Vol. 127, Iss. 21, pp. 9992-10000
Closed Access | Times Cited: 27
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
A triple atom catalyst with ultrahigh loading potential for nitrogen electrochemical reduction
Zhiwen Chen, Li Xin Chen, Ming Jiang, et al.
Journal of Materials Chemistry A (2020) Vol. 8, Iss. 30, pp. 15086-15093
Closed Access | Times Cited: 63
Stepping Out of Transition Metals: Activating the Dual Atomic Catalyst through Main Group Elements
Mingzi Sun, Hon Ho Wong, Tong Wu, et al.
Advanced Energy Materials (2021) Vol. 11, Iss. 30
Open Access | Times Cited: 46
Accelerated prediction of Cu-based single-atom alloy catalysts for CO2 reduction by machine learning
Dashuai Wang, Runfeng Cao, Shaogang Hao, et al.
Green Energy & Environment (2021) Vol. 8, Iss. 3, pp. 820-830
Open Access | Times Cited: 45
Dynamical Study of Adsorbate-Induced Restructuring Kinetics in Bimetallic Catalysts Using the PdAu(111) Model System
Chen Zhou, Hio Tong Ngan, Jin Soo Lim, et al.
Journal of the American Chemical Society (2022) Vol. 144, Iss. 33, pp. 15132-15142
Open Access | Times Cited: 33
Machine Learning-Aided Identification of Single Atom Alloy Catalysts
Aparajita Dasgupta, Yingjie Gao, Scott Broderick, et al.
The Journal of Physical Chemistry C (2020) Vol. 124, Iss. 26, pp. 14158-14166
Closed Access | Times Cited: 32
Machine Learning Enabled Screening of Single Atom Alloys: Predicting Reactivity Trend for Ethanol Dehydrogenation
Amrish Kumar, Jayendran Iyer, Fatima Jalid, et al.
ChemCatChem (2021) Vol. 14, Iss. 2
Closed Access | Times Cited: 24
Single Atom Alloys Aggregation in the Presence of Ligands
Maya Salem, Giannis Mpourmpakis
Nanoscale (2025)
Open Access
Kernel regression methods for prediction of materials properties: Recent developments
Ye Min Thant, Taishiro Wakamiya, Methawee Nukunudompanich, et al.
Chemical Physics Reviews (2025) Vol. 6, Iss. 1
Open Access
Machine learning assisted approximation of descriptors (CO and OH) binding energy on Cu-based bimetallic alloys
Pallavi Dandekar, Aditya Singh Ambesh, Tuhin Suvra Khan, et al.
Physical Chemistry Chemical Physics (2025)
Open Access
Machine learning enabled rational design of atomic catalysts for electrochemical reactions
Lianping Wu, Teng Li
Materials Chemistry Frontiers (2023) Vol. 7, Iss. 19, pp. 4445-4459
Closed Access | Times Cited: 9
Dual‐Site Metal Catalysts for Electrocatalytic CO2 Reduction Reaction
Li Liu, Xueting Wu, Fei Wang, et al.
Chemistry - A European Journal (2023) Vol. 29, Iss. 49
Closed Access | Times Cited: 9
Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis
Pushkar Ghanekar, Siddharth Deshpande, Jeffrey Greeley
(2021)
Open Access | Times Cited: 19
Single‐Atom Heterogeneous Catalysts: Human‐ and AI‐Driven Platform for Augmented Designs, Analytics and Reality‐Enabled Manufacturing
Lei Cheng, Yawen Tang, Kostya Ostrikov, et al.
Angewandte Chemie International Edition (2023) Vol. 63, Iss. 5
Closed Access | Times Cited: 8
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