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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 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

Showing 1-25 of 94 citing articles:

Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage
Daniel Rangel-Martínez, K.D.P. Nigam, Luis Ricardez‐Sandoval
Process Safety and Environmental Protection (2021) Vol. 174, pp. 414-441
Closed Access | Times Cited: 179
Sorption-enhanced Steam Methane Reforming for Combined CO2 Capture and Hydrogen Production: A State-of-the-Art Review
Salman Masoudi Soltani, Abhishek Lahiri, Husain Bahzad, et al.
Carbon Capture Science & Technology (2021) Vol. 1, pp. 100003-100003
Open Access | Times Cited: 154
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
Machine-Learning-Guided Discovery and Optimization of Additives in Preparing Cu Catalysts for CO2 Reduction
Ying Guo, Xinru He, Yuming Su, et al.
Journal of the American Chemical Society (2021) Vol. 143, Iss. 15, pp. 5755-5762
Closed Access | Times Cited: 113
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
Human- and machine-centred designs of molecules and materials for sustainability and decarbonization
Jiayu Peng, Daniel Schwalbe‐Koda, Karthik Akkiraju, et al.
Nature Reviews Materials (2022) Vol. 7, Iss. 12, pp. 991-1009
Closed Access | Times Cited: 91
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
Metal Alloys‐Structured Electrocatalysts: Metal–Metal Interactions, Coordination Microenvironments, and Structural Property–Reactivity Relationships
Chengdong Yang, Yun Gao, Tian Ma, et al.
Advanced Materials (2023) Vol. 35, Iss. 51
Open Access | Times Cited: 65
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
A strong bimetal-support interaction in ethanol steam reforming
Hao Meng, Yusen Yang, Tianyao Shen, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 46
Hybrid Double Atom Catalysts for Hydrogen Evolution Reaction: A Sweet Marriage of Metal and Nonmetal
Lihong Zhang, Xiangyu Guo, Shengli Zhang, et al.
Advanced Energy Materials (2023) Vol. 14, Iss. 2
Open Access | Times Cited: 46
Review and Outlook of Confined Ni Catalysts for the Dry Reforming of Methane Reaction
Yu Shi, Xiaoyan Tian, Zhiyong Deng, et al.
Energy & Fuels (2024) Vol. 38, Iss. 3, pp. 1633-1656
Closed Access | Times Cited: 34
From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design
Jorge Benavides-Hernández, Franck Dumeignil
ACS Catalysis (2024) Vol. 14, Iss. 15, pp. 11749-11779
Closed Access | Times Cited: 26
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
Targeted design of advanced electrocatalysts by machine learning
Letian Chen, Xu Zhang, An Chen, et al.
CHINESE JOURNAL OF CATALYSIS (CHINESE VERSION) (2021) Vol. 43, Iss. 1, pp. 11-32
Open Access | Times Cited: 87
tmQM Dataset—Quantum Geometries and Properties of 86k Transition Metal Complexes
David Balcells, Bastian Bjerkem Skjelstad
Journal of Chemical Information and Modeling (2020) Vol. 60, Iss. 12, pp. 6135-6146
Open Access | Times Cited: 80
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
High-Throughput Screening of Alloy Catalysts for Dry Methane Reforming
Yaxin Yu, Jie Yang, Kake Zhu, et al.
ACS Catalysis (2021) Vol. 11, Iss. 14, pp. 8881-8894
Closed Access | Times Cited: 68
Engineering Electrochemical Surface for Efficient Carbon Dioxide Upgrade
Guobin Wen, Bohua Ren, Yun Zheng, et al.
Advanced Energy Materials (2021) Vol. 12, Iss. 3
Closed Access | Times Cited: 66
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 activation energies of chemical reactions
Toby Lewis‐Atwell, Piers A. Townsend, Matthew N. Grayson
Wiley Interdisciplinary Reviews Computational Molecular Science (2021) Vol. 12, Iss. 4
Open Access | Times Cited: 61
Building up the “Genome” of bi-atom catalysts toward efficient HER/OER/ORR
Lihong Zhang, Xiangyu Guo, Shengli Zhang, et al.
Journal of Materials Chemistry A (2022) Vol. 10, Iss. 21, pp. 11600-11612
Closed Access | Times Cited: 58
Electrocatalytic Oxygen Reduction to Produce Hydrogen Peroxide: Rational Design from Single-Atom Catalysts to Devices
Yueyu Tong, Liqun Wang, Feng Hou, et al.
Electrochemical Energy Reviews (2022) Vol. 5, Iss. 3
Open Access | Times Cited: 50
Addressing complexity in catalyst design: From volcanos and scaling to more sophisticated design strategies
Sarah M. Stratton, Shengjie Zhang, M. M. Montemore
Surface Science Reports (2023) Vol. 78, Iss. 3, pp. 100597-100597
Open Access | Times Cited: 33
Data-Driven Discovery of Graphene-Based Dual-Atom Catalysts for Hydrogen Evolution Reaction with Graph Neural Network and DFT Calculations
Kajjana Boonpalit, Yutthana Wongnongwa, Chanatkran Prommin, et al.
ACS Applied Materials & Interfaces (2023) Vol. 15, Iss. 10, pp. 12936-12945
Closed Access | Times Cited: 28
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