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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:

Machine Learning in Catalysis, From Proposal to Practicing
Wenhong Yang, Timothy Tizhe Fidelis, Wen‐Hua Sun
ACS Omega (2019) Vol. 5, Iss. 1, pp. 83-88
Open Access | Times Cited: 161

Showing 1-25 of 161 citing articles:

Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm
Tanveer Ahmad, Rafał Madoński, Dongdong Zhang, et al.
Renewable and Sustainable Energy Reviews (2022) Vol. 160, pp. 112128-112128
Closed Access | Times Cited: 354
Electrocatalytic Hydrogenation of Biomass-Derived Organics: A Review
Sneha A. Akhade, Nirala Singh, Oliver Y. Gutiérrez, et al.
Chemical Reviews (2020) Vol. 120, Iss. 20, pp. 11370-11419
Closed Access | Times Cited: 290
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: 270
Applications of Zeolites to C1 Chemistry: Recent Advances, Challenges, and Opportunities
Qiang Zhang, Jihong Yu, Avelino Corma
Advanced Materials (2020) Vol. 32, Iss. 44
Open Access | Times Cited: 255
Heterogeneous activation of persulfate by metal and non-metal catalyst for the degradation of sulfamethoxazole: A review
Daniel T. Oyekunle, Eman A. Gendy, Jerosha Ifthikar, et al.
Chemical Engineering Journal (2022) Vol. 437, pp. 135277-135277
Closed Access | Times Cited: 232
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: 152
Surface and Interface Coordination Chemistry Learned from Model Heterogeneous Metal Nanocatalysts: From Atomically Dispersed Catalysts to Atomically Precise Clusters
Wentong Jing, Hui Shen, Ruixuan Qin, et al.
Chemical Reviews (2022) Vol. 123, Iss. 9, pp. 5948-6002
Closed Access | Times Cited: 129
Optimizing Catalyst and Reaction Conditions in Gold(I) Catalysis–Ligand Development
Alba Collado, David J. Nelson, Steven P. Nolan
Chemical Reviews (2021) Vol. 121, Iss. 14, pp. 8559-8612
Open Access | Times Cited: 123
Quantum dots: an overview of synthesis, properties, and applications
Kushagra Agarwal, Himanshu Rai, Sandip Mondal
Materials Research Express (2023) Vol. 10, Iss. 6, pp. 062001-062001
Open Access | Times Cited: 87
Recent Developments and Trends in Asymmetric Organocatalysis
Olga Garcı́a Mancheño, Mario Waser
European Journal of Organic Chemistry (2022) Vol. 26, Iss. 1
Open Access | Times Cited: 85
Machine‐Learning‐Assisted Nanozyme Design: Lessons from Materials and Engineered Enzymes
Jie Zhuang, Adam C. Midgley, Yonghua Wei, et al.
Advanced Materials (2023) Vol. 36, Iss. 10
Closed Access | Times Cited: 75
Experimental and Computational Synergistic Design of Cu and Fe Catalysts for the Reverse Water–Gas Shift: A Review
Ergys Pahija, Christopher Panaritis, Sergey Gusarov, et al.
ACS Catalysis (2022) Vol. 12, Iss. 12, pp. 6887-6905
Closed Access | Times Cited: 74
Atomic‐Level Design of Active Site on Two‐Dimensional MoS2 toward Efficient Hydrogen Evolution: Experiment, Theory, and Artificial Intelligence Modelling
Chunwen Sun, Longlu Wang, Weiwei Zhao, et al.
Advanced Functional Materials (2022) Vol. 32, Iss. 38
Closed Access | Times Cited: 73
Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures
Vera Kuznetsova, Áine Coogan, Dmitry Botov, et al.
Advanced Materials (2024) Vol. 36, Iss. 18
Open Access | Times Cited: 24
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: 24
Activity versus stability of atomically dispersed transition-metal electrocatalysts
Gang Wu, Piotr Zelenay
Nature Reviews Materials (2024) Vol. 9, Iss. 9, pp. 643-656
Closed Access | Times Cited: 20
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: 19
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: 18
Artificial intelligence interventions in 2D MXenes-based photocatalytic applications
Durga Madhab Mahapatra, Ashish Kumar, Rajesh Kumar, et al.
Coordination Chemistry Reviews (2025) Vol. 529, pp. 216460-216460
Closed Access | Times Cited: 2
Artificial Intelligence Enabled Biomineralization for Eco‐Friendly Nanomaterial Synthesis: Charting Future Trends
Vaisali Chandrasekar, Anu Jayanthi Panicker, Ajay Vikram Singh, et al.
Nano Select (2025)
Open Access | Times Cited: 2
Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry
Jon Paul Janet, Fang Liu, Aditya Nandy, et al.
Inorganic Chemistry (2019) Vol. 58, Iss. 16, pp. 10592-10606
Open Access | Times Cited: 119
Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts
Simone Gallarati, Raimón Fabregat, Rubén Laplaza, et al.
Chemical Science (2021) Vol. 12, Iss. 20, pp. 6879-6889
Open Access | Times Cited: 89
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: 87
Machine Learning in Screening High Performance Electrocatalysts for CO2 Reduction
Ning Zhang, Baopeng Yang, Kang Liu, et al.
Small Methods (2021) Vol. 5, Iss. 11
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: 69
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