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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:

Iterative experimental design based on active machine learning reduces the experimental burden associated with reaction screening
Natalie S. Eyke, William H. Green, Klavs F. Jensen
Reaction Chemistry & Engineering (2020) Vol. 5, Iss. 10, pp. 1963-1972
Open Access | Times Cited: 72

Showing 1-25 of 72 citing articles:

Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats
Maarten R. Dobbelaere, Pieter Plehiers, Ruben Van de Vijver, et al.
Engineering (2021) Vol. 7, Iss. 9, pp. 1201-1211
Open Access | Times Cited: 191
A Brief Introduction to Chemical Reaction Optimization
Connor J. Taylor, Alexander Pomberger, Kobi Felton, et al.
Chemical Reviews (2023) Vol. 123, Iss. 6, pp. 3089-3126
Open Access | Times Cited: 183
Prediction of chemical reaction yields using deep learning
Philippe Schwaller, Alain C. Vaucher, Teodoro Laino, et al.
Machine Learning Science and Technology (2021) Vol. 2, Iss. 1, pp. 015016-015016
Open Access | Times Cited: 166
Evidential Deep Learning for Guided Molecular Property Prediction and Discovery
Ava P. Soleimany, Alexander Amini, Samuel Goldman, et al.
ACS Central Science (2021) Vol. 7, Iss. 8, pp. 1356-1367
Open Access | Times Cited: 133
A review on machine learning algorithms for the ionic liquid chemical space
Spyridon Koutsoukos, Frederik Philippi, Francisco Malaret, et al.
Chemical Science (2021) Vol. 12, Iss. 20, pp. 6820-6843
Open Access | Times Cited: 118
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: 106
Reaction classification and yield prediction using the differential reaction fingerprint DRFP
Daniel Probst, Philippe Schwaller, Jean‐Louis Reymond
Digital Discovery (2022) Vol. 1, Iss. 2, pp. 91-97
Open Access | Times Cited: 106
Machine intelligence for chemical reaction space
Philippe Schwaller, Alain C. Vaucher, Rubén Laplaza, et al.
Wiley Interdisciplinary Reviews Computational Molecular Science (2022) Vol. 12, Iss. 5
Open Access | Times Cited: 87
Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery
Zhengkai Tu, Thijs Stuyver, Connor W. Coley
Chemical Science (2022) Vol. 14, Iss. 2, pp. 226-244
Open Access | Times Cited: 66
Self-Driving Laboratories for Chemistry and Materials Science
Gary Tom, Stefan P. Schmid, Sterling G. Baird, et al.
Chemical Reviews (2024) Vol. 124, Iss. 16, pp. 9633-9732
Open Access | Times Cited: 19
Unified Deep Learning Model for Multitask Reaction Predictions with Explanation
Jieyu Lü, Yingkai Zhang
Journal of Chemical Information and Modeling (2022) Vol. 62, Iss. 6, pp. 1376-1387
Open Access | Times Cited: 52
The effect of chemical representation on active machine learning towards closed-loop optimization
Alexander Pomberger, A. A. Pedrina McCarthy, Asif Iqbal Khan, et al.
Reaction Chemistry & Engineering (2022) Vol. 7, Iss. 6, pp. 1368-1379
Open Access | Times Cited: 43
Design and Application of a Screening Set for Monophosphine Ligands in Cross-Coupling
Tobias Gensch, Sleight R. Smith, Thomas J. Colacot, et al.
ACS Catalysis (2022) Vol. 12, Iss. 13, pp. 7773-7780
Closed Access | Times Cited: 41
Continuous stirred-tank reactor cascade platform for self-optimization of reactions involving solids
Kakasaheb Y. Nandiwale, Travis Hart, Andrew F. Zahrt, et al.
Reaction Chemistry & Engineering (2022) Vol. 7, Iss. 6, pp. 1315-1327
Open Access | Times Cited: 40
Predicting reaction conditions from limited data through active transfer learning
Eunjae Shim, Joshua Kammeraad, Ziping Xu, et al.
Chemical Science (2022) Vol. 13, Iss. 22, pp. 6655-6668
Open Access | Times Cited: 36
Dataset Design for Building Models of Chemical Reactivity
Priyanka Raghavan, Brittany C. Haas, Madeline E. Ruos, et al.
ACS Central Science (2023) Vol. 9, Iss. 12, pp. 2196-2204
Open Access | Times Cited: 31
Explainable uncertainty quantifications for deep learning-based molecular property prediction
Chu-I Yang, Yi‐Pei Li
Journal of Cheminformatics (2023) Vol. 15, Iss. 1
Open Access | Times Cited: 27
Calibrated geometric deep learning improves kinase–drug binding predictions
Yunan Luo, Yang Liu, Jian Peng
Nature Machine Intelligence (2023) Vol. 5, Iss. 12, pp. 1390-1401
Open Access | Times Cited: 21
Applying statistical modeling strategies to sparse datasets in synthetic chemistry
Brittany C. Haas, Dipannita Kalyani, Matthew S. Sigman
Science Advances (2025) Vol. 11, Iss. 1
Closed Access
Uncertainty quantification in drug design
Lewis Mervin, Simon Johansson, Elizaveta Semenova, et al.
Drug Discovery Today (2020) Vol. 26, Iss. 2, pp. 474-489
Closed Access | Times Cited: 58
Chemical data intelligence for sustainable chemistry
Jana M. Weber, Zhen Guo, Chonghuan Zhang, et al.
Chemical Society Reviews (2021) Vol. 50, Iss. 21, pp. 12013-12036
Open Access | Times Cited: 41
Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit
Eunjae Shim, Ambuj Tewari, Tim Cernak, et al.
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 12, pp. 3659-3668
Open Access | Times Cited: 16
When Yield Prediction Does Not Yield Prediction: An Overview of the Current Challenges
Varvara Voinarovska, Mikhail A. Kabeshov, Dmytro Dudenko, et al.
Journal of Chemical Information and Modeling (2023) Vol. 64, Iss. 1, pp. 42-56
Closed Access | Times Cited: 14
Machine learning-guided yield optimization for palladaelectro-catalyzed annulation reaction
Xiaoyan Hou, Shuwen Li, Johanna Frey, et al.
Chem (2024) Vol. 10, Iss. 7, pp. 2283-2294
Open Access | Times Cited: 5
Automation and machine learning augmented by large language models in a catalysis study
Yuming Su, Xue Wang, Yuanxiang Ye, et al.
Chemical Science (2024) Vol. 15, Iss. 31, pp. 12200-12233
Open Access | Times Cited: 5
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