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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:

A unified machine-learning protocol for asymmetric catalysis as a proof of concept demonstration using asymmetric hydrogenation
Sukriti Singh, Monika Pareek, Avtar Changotra, et al.
Proceedings of the National Academy of Sciences (2020) Vol. 117, Iss. 3, pp. 1339-1345
Open Access | Times Cited: 108

Showing 1-25 of 108 citing articles:

A review on extreme learning machine
Jian Wang, Siyuan Lu, Shuihua Wang‎, et al.
Multimedia Tools and Applications (2021) Vol. 81, Iss. 29, pp. 41611-41660
Open Access | Times Cited: 322
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
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
Toward Machine Learning-Enhanced High-Throughput Experimentation
Natalie S. Eyke, Brent A. Koscher, Klavs F. Jensen
Trends in Chemistry (2021) Vol. 3, Iss. 2, pp. 120-132
Open Access | Times Cited: 112
Evaluation guidelines for machine learning tools in the chemical sciences
Andreas Bender, Nadine Schneider, Marwin Segler, et al.
Nature Reviews Chemistry (2022) Vol. 6, Iss. 6, pp. 428-442
Closed Access | Times Cited: 104
Enantioselectivity prediction of pallada-electrocatalysed C–H activation using transition state knowledge in machine learning
Li‐Cheng Xu, Johanna Frey, Xiaoyan Hou, et al.
Nature Synthesis (2023) Vol. 2, Iss. 4, pp. 321-330
Closed Access | Times Cited: 48
Machine learning advancements in organic synthesis: A focused exploration of artificial intelligence applications in chemistry
Rizvi Syed Aal E Ali, Jiaolong Meng, Muhammad Ehtisham Ibraheem Khan, et al.
Artificial Intelligence Chemistry (2024) Vol. 2, Iss. 1, pp. 100049-100049
Open Access | Times Cited: 25
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 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
Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors
Yanfei Guan, Connor W. Coley, Haoyang Wu, et al.
Chemical Science (2020) Vol. 12, Iss. 6, pp. 2198-2208
Open Access | Times Cited: 108
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
Artificial intelligence: machine learning for chemical sciences
A. Karthikeyan, U. Deva Priyakumar
Journal of Chemical Sciences (2021) Vol. 134, Iss. 1
Open Access | Times Cited: 66
Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors**
Nobuya Tsuji, Pavel Sidorov, Chendan Zhu, et al.
Angewandte Chemie International Edition (2023) Vol. 62, Iss. 11
Open Access | Times Cited: 41
Machine learning integrated photocatalysis: progress and challenges
Luyao Ge, Yuanzhen Ke, Xiaobo Li
Chemical Communications (2023) Vol. 59, Iss. 39, pp. 5795-5806
Closed Access | Times Cited: 37
Molecular Machine Learning for Chemical Catalysis: Prospects and Challenges
Sukriti Singh, Raghavan B. Sunoj
Accounts of Chemical Research (2023) Vol. 56, Iss. 3, pp. 402-412
Closed Access | Times Cited: 35
High-Throughput Computational Screening of Bioinspired Dual-Atom Alloys for CO2 Activation
Drew Behrendt, Sayan Banerjee, Cole Clark, et al.
Journal of the American Chemical Society (2023) Vol. 145, Iss. 8, pp. 4730-4735
Closed Access | Times Cited: 34
Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge
Shu-Wen Li, Li‐Cheng Xu, Cheng Zhang, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 27
Paving the road towards automated homogeneous catalyst design
Adarsh V. Kalikadien, A.H. Mirza, Aydin Najl Hossaini, et al.
ChemPlusChem (2024) Vol. 89, Iss. 7
Open Access | Times Cited: 12
AI for organic and polymer synthesis
Hong Xin, Qi Yang, Kuangbiao Liao, et al.
Science China Chemistry (2024) Vol. 67, Iss. 8, pp. 2461-2496
Closed Access | Times Cited: 11
When Do Quantum Mechanical Descriptors Help Graph Neural Networks to Predict Chemical Properties?
Shih‐Cheng Li, Haoyang Wu, Angiras Menon, et al.
Journal of the American Chemical Society (2024) Vol. 146, Iss. 33, pp. 23103-23120
Closed Access | Times Cited: 10
HCat-GNet: a Human-Interpretable GNN Tool for Ligand Optimization in Asymmetric Catalysis
Eduardo Alberto Aguilar Bejarano, Ender Özcan, Raja K. Rit, et al.
iScience (2025) Vol. 28, Iss. 3, pp. 111881-111881
Open Access | Times Cited: 1
Machine learning in experimental materials chemistry
Balaranjan Selvaratnam, Ranjit T. Koodali
Catalysis Today (2020) Vol. 371, pp. 77-84
Open Access | Times Cited: 52
Dreams, False Starts, Dead Ends, and Redemption: A Chronicle of the Evolution of a Chemoinformatic Workflow for the Optimization of Enantioselective Catalysts
N. Ian Rinehart, Andrew F. Zahrt, Jeremy Henle, et al.
Accounts of Chemical Research (2021) Vol. 54, Iss. 9, pp. 2041-2054
Closed Access | Times Cited: 48
Reducing Challenges in Organic Synthesis with Stereoselective Hydrogenation and Tandem Catalysis
Patrick D. Parker, Xintong Hou, Vy M. Dong
Journal of the American Chemical Society (2021) Vol. 143, Iss. 18, pp. 6724-6745
Open Access | Times Cited: 44
Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning
Li‐Cheng Xu, Shuo‐Qing Zhang, Xin Li, et al.
Angewandte Chemie International Edition (2021) Vol. 60, Iss. 42, pp. 22804-22811
Closed Access | Times Cited: 42
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