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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:

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: 98

Showing 1-25 of 98 citing articles:

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: 292
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
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: 168
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: 136
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: 109
Self-driving laboratories: A paradigm shift in nanomedicine development
Riley J. Hickman, Pauric Bannigan, Zeqing Bao, et al.
Matter (2023) Vol. 6, Iss. 4, pp. 1071-1081
Open Access | Times Cited: 38
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: 18
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: 13
Identifying general reaction conditions by bandit optimization
Jason Y. Wang, Jason M. Stevens, Stavros K. Kariofillis, et al.
Nature (2024) Vol. 626, Iss. 8001, pp. 1025-1033
Open Access | Times Cited: 13
Self‐Driving Platform for Metal Nanoparticle Synthesis: Combining Microfluidics and Machine Learning
Huachen Tao, Tianyi Wu, Sina Kheiri, et al.
Advanced Functional Materials (2021) Vol. 31, Iss. 51
Open Access | Times Cited: 86
Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation
Yunchao Xie, Kianoosh Sattari, Chi Zhang, et al.
Progress in Materials Science (2022) Vol. 132, pp. 101043-101043
Open Access | Times Cited: 52
Bayesian optimization with known experimental and design constraints for chemistry applications
Riley J. Hickman, Matteo Aldeghi, Florian Häse, et al.
Digital Discovery (2022) Vol. 1, Iss. 5, pp. 732-744
Open Access | Times Cited: 48
Machine Learning C–N Couplings: Obstacles for a General-Purpose Reaction Yield Prediction
Martin Fitzner, Georg Wuitschik, Raffael Koller, et al.
ACS Omega (2023) Vol. 8, Iss. 3, pp. 3017-3025
Open Access | Times Cited: 33
Continuous flow synthesis of pyridinium salts accelerated by multi-objective Bayesian optimization with active learning
John H. Dunlap, Jeffrey G. Ethier, Amelia A. Putnam‐Neeb, et al.
Chemical Science (2023) Vol. 14, Iss. 30, pp. 8061-8069
Open Access | Times Cited: 27
A Review on Artificial Intelligence Enabled Design, Synthesis, and Process Optimization of Chemical Products for Industry 4.0
Chasheng He, Chengwei Zhang, Tengfei Bian, et al.
Processes (2023) Vol. 11, Iss. 2, pp. 330-330
Open Access | Times Cited: 25
Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS
Gary Tom, Riley J. Hickman, Aniket Zinzuwadia, et al.
Digital Discovery (2023) Vol. 2, Iss. 3, pp. 759-774
Open Access | Times Cited: 23
Automated Transition Metal Catalysts Discovery and Optimisation with AI and Machine Learning
S. Macé, Yingjian Xu, Bao N. Nguyen
ChemCatChem (2024) Vol. 16, Iss. 10
Open Access | Times Cited: 7
Multi-Variable Multi-Metric Optimization of Self-Assembled Photocatalytic CO2 Reduction Performance Using Machine Learning Algorithms
Shannon A. Bonke, Giovanni Trezza, Luca Bergamasco, et al.
Journal of the American Chemical Society (2024) Vol. 146, Iss. 22, pp. 15648-15658
Open Access | Times Cited: 7
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: 7
A mini review on the applications of artificial intelligence (AI) in surface chemistry and catalysis
Faisal Al-Akayleh, Ahmed S.A. Ali Agha, Rami A. Abdel Rahem, et al.
Tenside Surfactants Detergents (2024) Vol. 61, Iss. 4, pp. 285-296
Closed Access | Times Cited: 6
Accelerating the Development of Sustainable Catalytic Processes through Data Science
Jason M. Stevens, Jacob M. Ganley, Matthew J. Goldfogel, et al.
Organic Process Research & Development (2025)
Closed Access
Implementation of a Bayesian Optimization Framework for Interconnected Systems
Leonardo D. González, Ví­ctor M. Zavala
Industrial & Engineering Chemistry Research (2025)
Closed Access
Categorical data clustering: 25 years beyond K-modes
Duy-Tai Dinh, Wong Hauchi, Philippe Fournier‐Viger, et al.
Expert Systems with Applications (2025), pp. 126608-126608
Open Access
Pairwise Difference Regression: A Machine Learning Meta-algorithm for Improved Prediction and Uncertainty Quantification in Chemical Search
Michael Tynes, Wenhao Gao, Daniel J. Burrill, et al.
Journal of Chemical Information and Modeling (2021) Vol. 61, Iss. 8, pp. 3846-3857
Open Access | Times Cited: 41
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