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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:

Bayesian optimization-driven parallel-screening of multiple parameters for the flow synthesis of biaryl compounds
Masaru Kondo, H. D. P. Wathsala, Mohamed S. H. Salem, et al.
Communications Chemistry (2022) Vol. 5, Iss. 1
Open Access | Times Cited: 21

Showing 21 citing articles:

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
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: 28
Bayesian optimisation for efficient material discovery: a mini review
Yimeng Jin, Priyank V. Kumar
Nanoscale (2023) Vol. 15, Iss. 26, pp. 10975-10984
Closed Access | Times Cited: 18
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
Advancements in Machine Learning Predicting Activation and Gibbs Free Energies in Chemical Reactions
Guo‐Jin Cao
International Journal of Quantum Chemistry (2025) Vol. 125, Iss. 7
Closed Access
Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights
Yuxinxin Chen, Yanchi Ou, Peikun Zheng, et al.
The Journal of Chemical Physics (2023) Vol. 158, Iss. 7
Closed Access | Times Cited: 12
External Flash Generation of Carbenoids Enables Monodeuteration of Dihalomethanes
Kazuhiro Okamoto, Ryosuke Higuma, Kensuke Muta, et al.
Chemistry - A European Journal (2023) Vol. 29, Iss. 47
Closed Access | Times Cited: 12
Optimizing microflow sequential coupling and cyclization of multiple linear substrates guided by Bayesian optimization
Shinichiro Fuse, Kohei Nakabayashi, Naoto Sugisawa, et al.
Bulletin of the Chemical Society of Japan (2025) Vol. 98, Iss. 4
Open Access
Synthesis and Structural and Optical Behavior of Dehydrohelicene-Containing Polycyclic Compounds
Md. Imrul Khalid, Mohamed S. H. Salem, Shinobu Takizawa
Molecules (2024) Vol. 29, Iss. 2, pp. 296-296
Open Access | Times Cited: 3
COBRA web application to benchmark linear regression models for catalyst optimization with few-entry datasets
Zhen Cao, Laura Falivene, Albert Poater, et al.
Cell Reports Physical Science (2024), pp. 102348-102348
Open Access | Times Cited: 3
Understanding the Manufacturing Process of Lipid Nanoparticles for mRNA Delivery Using Machine Learning
Shinya Sato, Syusuke Sano, Hiroki Muto, et al.
Chemical and Pharmaceutical Bulletin (2024) Vol. 72, Iss. 6, pp. 529-539
Open Access | Times Cited: 2
Data-driven Electrochemical One-pot Synthesis of Double Hetero[7]dehydrohelicene
Mohamed S. H. Salem, R. D. SHARMA, Md. Imrul Khalid, et al.
Electrochemistry (2023) Vol. 91, Iss. 11, pp. 112015-112015
Open Access | Times Cited: 6
Machine Learning that Proposes Reaction Conditions and Yields for Wittig-type Methylenation of Aldehydes with Bis(iodozincio)methane in a Flow-microreactor
Taiyo Maruoka, Akira Yada, Kazuhiko Sato, et al.
Chemistry Letters (2023) Vol. 52, Iss. 5, pp. 397-399
Closed Access | Times Cited: 4
Bayesian Optimization-Assisted Screening to Identify Improved Reaction Conditions for Spiro-Dithiolane Synthesis
Masaru Kondo, H. D. P. Wathsala, Kazunori Ishikawa, et al.
Molecules (2023) Vol. 28, Iss. 13, pp. 5180-5180
Open Access | Times Cited: 4
Self-Optimizing Bayesian for Continuous Flow Synthesis Process
Runzhe Liu, Zihao Wang, Wenbo Yang, et al.
Digital Discovery (2024)
Open Access | Times Cited: 1
Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis
Stefan P. Schmid, Leon Schlosser, Frank Glorius, et al.
Beilstein Journal of Organic Chemistry (2024) Vol. 20, pp. 2280-2304
Open Access | Times Cited: 1
A Review of the Applications of Artificial Intelligence in the Process Analysis and Optimization of Chemical Products
Runqiu Shen, Weike Su
Pharmaceutical Fronts (2023) Vol. 05, Iss. 04, pp. e219-e226
Open Access | Times Cited: 2
Bayesian optimization assisted screening conditions for visible light-induced hydroxy-perfluoroalkylation
Koto Tagami, Masaru Kondo, Shinobu Takizawa, et al.
Journal of Fluorine Chemistry (2024) Vol. 276, pp. 110294-110294
Open Access
Continuous flow process optimization aided by machine learning for a pharmaceutical intermediate
Jinlin Zhu, Chenyang Zhao, Li Sheng, et al.
Journal of Flow Chemistry (2024) Vol. 14, Iss. 3, pp. 539-546
Closed Access
Process-constrained batch Bayesian approaches for yield optimization in multi-reactor systems
Markus Grimm, Sébastien Paul, Pierre Chainais
Computers & Chemical Engineering (2024) Vol. 189, pp. 108779-108779
Closed Access
Vanadium‐Catalyzed Atroposelective Coupling of Arenols and Application in the Synthesis of Polycyclic Heteroaromatics ( PHAs )
Mohamed S. H. Salem, Shinobu Takizawa
(2024), pp. 45-68
Closed Access
Reaction of Highly Volatile Organic Compounds with Organolithium Species in Flow Microreactor
Aiichiro Nagaki, Kensuke Muta, Kazuhiro Okamoto
Synlett (2023)
Closed Access | Times Cited: 1
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