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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:

Autonomous Discovery in the Chemical Sciences Part II: Outlook
Connor W. Coley, Natalie S. Eyke, Klavs F. Jensen
Angewandte Chemie International Edition (2019) Vol. 59, Iss. 52, pp. 23414-23436
Open Access | Times Cited: 236

Showing 1-25 of 236 citing articles:

Scientific discovery in the age of artificial intelligence
Hanchen Wang, Tianfan Fu, Yuanqi Du, et al.
Nature (2023) Vol. 620, Iss. 7972, pp. 47-60
Closed Access | Times Cited: 702
Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery
Andrew Rosen, Shaelyn Iyer, Debmalya Ray, et al.
Matter (2021) Vol. 4, Iss. 5, pp. 1578-1597
Open Access | Times Cited: 313
Bimetallic Sites for Catalysis: From Binuclear Metal Sites to Bimetallic Nanoclusters and Nanoparticles
Lichen Liu, Avelino Corma
Chemical Reviews (2023) Vol. 123, Iss. 8, pp. 4855-4933
Open Access | Times Cited: 260
Rechargeable Batteries of the Future—The State of the Art from a BATTERY 2030+ Perspective
Maximilian Fichtner, Kristina Edström, Elixabete Ayerbe, et al.
Advanced Energy Materials (2021) Vol. 12, Iss. 17
Open Access | Times Cited: 238
A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis
Tobias Gensch, Gabriel dos Passos Gomes, Pascal Friederich, et al.
Journal of the American Chemical Society (2022) Vol. 144, Iss. 3, pp. 1205-1217
Closed Access | Times Cited: 220
De novo molecular design and generative models
Joshua Meyers, Benedek Fabian, Nathan Brown
Drug Discovery Today (2021) Vol. 26, Iss. 11, pp. 2707-2715
Open Access | Times Cited: 214
Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning
Aditya Nandy, Chenru Duan, Michael G. Taylor, et al.
Chemical Reviews (2021) Vol. 121, Iss. 16, pp. 9927-10000
Closed Access | Times Cited: 213
Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis
Thomas J. Struble, Juan C. Alvarez, Scott P. Brown, et al.
Journal of Medicinal Chemistry (2020) Vol. 63, Iss. 16, pp. 8667-8682
Open Access | Times Cited: 196
A field guide to flow chemistry for synthetic organic chemists
Luca Capaldo, Zhenghui Wen, Timothy Noël
Chemical Science (2023) Vol. 14, Iss. 16, pp. 4230-4247
Open Access | Times Cited: 189
Deep Learning in Protein Structural Modeling and Design
Wenhao Gao, Sai Pooja Mahajan, Jeremias Sulam, et al.
Patterns (2020) Vol. 1, Iss. 9, pp. 100142-100142
Open Access | Times Cited: 184
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
John A. Keith, Valentín Vassilev-Galindo, Bingqing Cheng, et al.
Chemical Reviews (2021) Vol. 121, Iss. 16, pp. 9816-9872
Open Access | Times Cited: 179
Data-science driven autonomous process optimization
Melodie Christensen, Lars P. E. Yunker, Folarin Adedeji, et al.
Communications Chemistry (2021) Vol. 4, Iss. 1
Open Access | Times Cited: 175
Machine Learning for Chemical Reactivity: The Importance of Failed Experiments
Felix Strieth‐Kalthoff, Frederik Sandfort, Marius Kühnemund, et al.
Angewandte Chemie International Edition (2022) Vol. 61, Iss. 29
Closed Access | Times Cited: 148
Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab
Martin Seifrid, Robert Pollice, Andrés Aguilar-Gránda, et al.
Accounts of Chemical Research (2022) Vol. 55, Iss. 17, pp. 2454-2466
Open Access | Times Cited: 147
Predicting Reaction Yields via Supervised Learning
A. Zuranski, Jesus I. Martinez Alvarado, Benjamin J. Shields, et al.
Accounts of Chemical Research (2021) Vol. 54, Iss. 8, pp. 1856-1865
Closed Access | Times Cited: 144
Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies
Kjell Jorner, Tore Brinck, Per‐Ola Norrby, et al.
Chemical Science (2020) Vol. 12, Iss. 3, pp. 1163-1175
Open Access | Times Cited: 142
Organic reactivity from mechanism to machine learning
Kjell Jorner, Anna Tomberg, Christoph Bauer, et al.
Nature Reviews Chemistry (2021) Vol. 5, Iss. 4, pp. 240-255
Closed Access | Times Cited: 142
Exploration of Ultralarge Compound Collections for Drug Discovery
Wendy A. Warr, Marc C. Nicklaus, Christos A. Nicolaou, et al.
Journal of Chemical Information and Modeling (2022) Vol. 62, Iss. 9, pp. 2021-2034
Closed Access | Times Cited: 118
Bayesian Optimization of Computer-Proposed Multistep Synthetic Routes on an Automated Robotic Flow Platform
Anirudh M. K. Nambiar, C. Breen, Travis Hart, et al.
ACS Central Science (2022) Vol. 8, Iss. 6, pp. 825-836
Open Access | Times Cited: 117
The Evolution of Data-Driven Modeling in Organic Chemistry
Wendy L. Williams, Lingyu Zeng, Tobias Gensch, et al.
ACS Central Science (2021) Vol. 7, Iss. 10, pp. 1622-1637
Open Access | Times Cited: 114
Recent advances in computational modeling of MOFs: From molecular simulations to machine learning
Hakan Demir, Hilal Daglar, Hasan Can Gülbalkan, et al.
Coordination Chemistry Reviews (2023) Vol. 484, pp. 215112-215112
Open Access | Times Cited: 114
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: 111
Chemical language models enable navigation in sparsely populated chemical space
Michael A. Skinnider, R. Greg Stacey, David S. Wishart, et al.
Nature Machine Intelligence (2021) Vol. 3, Iss. 9, pp. 759-770
Closed Access | Times Cited: 109
Toward autonomous design and synthesis of novel inorganic materials
Nathan J. Szymanski, Yan Zeng, Haoyan Huo, et al.
Materials Horizons (2021) Vol. 8, Iss. 8, pp. 2169-2198
Open Access | Times Cited: 107
Gryffin: An algorithm for Bayesian optimization of categorical variables informed by expert knowledge
Florian Häse, Matteo Aldeghi, Riley J. Hickman, et al.
Applied Physics Reviews (2021) Vol. 8, Iss. 3
Open Access | Times Cited: 104
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