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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:

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

Showing 1-25 of 175 citing articles:

Data-Driven Strategies for Accelerated Materials Design
Robert Pollice, Gabriel dos Passos Gomes, Matteo Aldeghi, et al.
Accounts of Chemical Research (2021) Vol. 54, Iss. 4, pp. 849-860
Open Access | Times Cited: 317
Nanoparticle synthesis assisted by machine learning
Huachen Tao, Tianyi Wu, Matteo Aldeghi, et al.
Nature Reviews Materials (2021) Vol. 6, Iss. 8, pp. 701-716
Closed Access | Times Cited: 313
Interpretable machine learning for knowledge generation in heterogeneous catalysis
Jacques A. Esterhuizen, Bryan R. Goldsmith, Suljo Linic
Nature Catalysis (2022) Vol. 5, Iss. 3, pp. 175-184
Closed Access | Times Cited: 243
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
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: 198
Machine learning directed drug formulation development
Pauric Bannigan, Matteo Aldeghi, Zeqing Bao, et al.
Advanced Drug Delivery Reviews (2021) Vol. 175, pp. 113806-113806
Closed Access | Times Cited: 184
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
Closed-loop optimization of general reaction conditions for heteroaryl Suzuki-Miyaura coupling
Nicholas H. Angello, Vandana Rathore, Wiktor Beker, et al.
Science (2022) Vol. 378, Iss. 6618, pp. 399-405
Closed Access | Times Cited: 126
A self-driving laboratory advances the Pareto front for material properties
Benjamin P. MacLeod, Fraser G. L. Parlane, Connor C. Rupnow, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open 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
A Multi-Objective Active Learning Platform and Web App for Reaction Optimization
José Antonio Garrido Torres, Sii Hong Lau, Pranay Anchuri, et al.
Journal of the American Chemical Society (2022) Vol. 144, Iss. 43, pp. 19999-20007
Open Access | Times Cited: 115
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
Emerging Trends in Cross-Coupling: Twelve-Electron-Based L1Pd(0) Catalysts, Their Mechanism of Action, and Selected Applications
Sharbil J. Firsan, Vilvanathan Sivakumar, Thomas J. Colacot
Chemical Reviews (2022) Vol. 122, Iss. 23, pp. 16983-17027
Open Access | Times Cited: 101
From Platform to Knowledge Graph: Evolution of Laboratory Automation
Jiaru Bai, Liwei Cao, Sebastian Mosbach, et al.
JACS Au (2022) Vol. 2, Iss. 2, pp. 292-309
Open Access | Times Cited: 70
Self-Driving Laboratory for Polymer Electronics
Aikaterini Vriza, Henry Chan, Jie Xu
Chemistry of Materials (2023) Vol. 35, Iss. 8, pp. 3046-3056
Closed Access | Times Cited: 47
Perspectives for self-driving labs in synthetic biology
Héctor García Martín, Tijana Radivojević, Jeremy Zucker, et al.
Current Opinion in Biotechnology (2023) Vol. 79, pp. 102881-102881
Open Access | Times Cited: 43
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: 33
Delocalized, asynchronous, closed-loop discovery of organic laser emitters
Felix Strieth‐Kalthoff, Han Hao, Vandana Rathore, et al.
Science (2024) Vol. 384, Iss. 6697
Closed Access | Times Cited: 27
Autonomous reaction Pareto-front mapping with a self-driving catalysis laboratory
Jeffrey A. Bennett, Negin Orouji, Muhammad Babar Khan, et al.
Nature Chemical Engineering (2024) Vol. 1, Iss. 3, pp. 240-250
Open Access | Times Cited: 17
Accelerated exploration of heterogeneous CO2 hydrogenation catalysts by Bayesian-optimized high-throughput and automated experimentation
Adrián Ramírez, Erwin Lam, Daniel Pacheco Gutiérrez, et al.
Chem Catalysis (2024) Vol. 4, Iss. 2, pp. 100888-100888
Closed Access | Times Cited: 16
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 | Times Cited: 2
Automation isn't automatic
Melodie Christensen, Lars P. E. Yunker, Parisa Shiri, et al.
Chemical Science (2021) Vol. 12, Iss. 47, pp. 15473-15490
Open Access | Times Cited: 89
Data Science Meets Physical Organic Chemistry
Jennifer M. Crawford, Cian Kingston, F. Dean Toste, et al.
Accounts of Chemical Research (2021) Vol. 54, Iss. 16, pp. 3136-3148
Open Access | Times Cited: 79
Bayesian optimization of nanoporous materials
Aryan Deshwal, Cory M. Simon, Janardhan Rao Doppa
Molecular Systems Design & Engineering (2021) Vol. 6, Iss. 12, pp. 1066-1086
Open Access | Times Cited: 69
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