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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:

Catalysts informatics: paradigm shift towards data-driven catalyst design
Keisuke Takahashi, Junya Ohyama, Shun Nishimura, et al.
Chemical Communications (2023) Vol. 59, Iss. 16, pp. 2222-2238
Open Access | Times Cited: 37

Showing 1-25 of 37 citing articles:

Advancing electrocatalytic reactions through mapping key intermediates to active sites via descriptors
Xiaowen Sun, Rafael B. Araujo, Egon Campos dos Santos, et al.
Chemical Society Reviews (2024) Vol. 53, Iss. 14, pp. 7392-7425
Closed Access | Times Cited: 18
Recent advances in catalyst design, performance, and challenges of metal-heteroatom-co-doped biochar as peroxymonosulfate activator for environmental remediation
Ganapaty Manickavasagam, Chao He, Kun‐Yi Andrew Lin, et al.
Environmental Research (2024) Vol. 252, pp. 118919-118919
Closed Access | Times Cited: 17
A machine learning framework for accelerating the development of highly efficient methanol synthesis catalysts
Weixian Li, Yi Dong, Mingchu Ran, et al.
Journal of Energy Chemistry (2025)
Closed Access | Times Cited: 3
Interpretable machine learning framework for catalyst performance prediction and validation with dry reforming of methane
Jiwon Roh, Hyundo Park, Hyukwon Kwon, et al.
Applied Catalysis B Environment and Energy (2023) Vol. 343, pp. 123454-123454
Open Access | Times Cited: 24
Automatic feature engineering for catalyst design using small data without prior knowledge of target catalysis
Toshiaki Taniike, Aya Fujiwara, Sunao Nakanowatari, et al.
Communications Chemistry (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 15
Catalysis in the digital age: Unlocking the power of data with machine learning
B. Moses Abraham, M. V. Jyothirmai, Priyanka Sinha, et al.
Wiley Interdisciplinary Reviews Computational Molecular Science (2024) Vol. 14, Iss. 5
Open Access | Times Cited: 9
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: 8
Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use
Clara Patricia Marshall, Julia Schumann, Annette Trunschke
Angewandte Chemie International Edition (2023) Vol. 62, Iss. 30
Open Access | Times Cited: 16
Hybrid Quantum Neural Network Model with Catalyst Experimental Validation: Application for the Dry Reforming of Methane
Jiwon Roh, Seunghyeon Oh, Donggyun Lee, et al.
ACS Sustainable Chemistry & Engineering (2024) Vol. 12, Iss. 10, pp. 4121-4131
Closed Access | Times Cited: 4
A review on Fe2O3‐based catalysts for toluene oxidation: catalysts design and optimization with the formation of abundant oxygen vacancies
Ruoqun Zhang, Hongzhe He, Yuneng Tang, et al.
ChemCatChem (2024)
Open Access | Times Cited: 4
An auto-configurable and interpretable ensemble learning framework for optimal catalyst design of green methanol production via Bayesian optimization
Dongwen Rong, Zhao Wang, Qiwen Guo, et al.
Journal of Cleaner Production (2025) Vol. 488, pp. 144666-144666
Closed Access
Machine learning knowledge‐driven Pt‐based catalyst design library development for selective oxidation of polyalcohol
Xin Zhou, Qin Haoli, Zhibo Zhang, et al.
AIChE Journal (2025)
Closed Access
Optimising Materials Properties with Minimal Data: Lessons from Vanadium Catalyst Modelling
José Ferraz-Caetano, Filipe Teixeira, M. Natália D. S. Cordeiro
Challenges and advances in computational chemistry and physics (2025), pp. 117-138
Closed Access
Guided electrocatalyst design through in-situ techniques and data mining approaches
Mingyu Ma, Yuqing Wang, Yanting Liu, et al.
Nano Convergence (2025) Vol. 12, Iss. 1
Open Access
Catalyst breakthroughs in methane dry reforming: Employing machine learning for future advancements
Somavia Ameen, Muhammad Umar Farooq, Samia, et al.
International Journal of Hydrogen Energy (2024)
Closed Access | Times Cited: 3
Leveraging machine learning engineering to uncover insights into heterogeneous catalyst design for oxidative coupling of methane
Shun Nishimura, Xinyue Li, Junya Ohyama, et al.
Catalysis Science & Technology (2023) Vol. 13, Iss. 16, pp. 4646-4655
Open Access | Times Cited: 8
Toward three-dimensionally ordered nanoporous graphene materials: template synthesis, structure, and applications
Masanori Yamamoto, Shunsuke Goto, Rui Tang, et al.
Chemical Science (2023) Vol. 15, Iss. 6, pp. 1953-1965
Open Access | Times Cited: 8
Navigating epoxidation complexity: building a data science toolbox to design vanadium catalysts
José Ferraz-Caetano, Filipe Teixeira, M. Natália D. S. Cordeiro
New Journal of Chemistry (2024) Vol. 48, Iss. 12, pp. 5097-5100
Open Access | Times Cited: 2
Synthesis and catalytic application of nanostructured metal oxides and phosphates
Keigo Kamata, Takeshi Aihara, Keiju Wachi
Chemical Communications (2024) Vol. 60, Iss. 81, pp. 11483-11499
Open Access | Times Cited: 2
Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use
Clara Patricia Marshall, Julia Schumann, Annette Trunschke
Angewandte Chemie (2023) Vol. 135, Iss. 30
Open Access | Times Cited: 5
Exploring Metal Nanocluster Catalysts for Ammonia Synthesis Using Informatics Methods: A Concerted Effort of Bayesian Optimization, Swarm Intelligence, and First-Principles Computation
Yuta Tsuji, Yuta Yoshioka, Kazuki Okazawa, et al.
ACS Omega (2023) Vol. 8, Iss. 33, pp. 30335-30348
Open Access | Times Cited: 5
Development of graphical user interface for design of experiments via Gaussian process regression and its case study
Yoshiki Hasukawa, Micke Kuwahara, Lauren Takahashi, et al.
Science and Technology of Advanced Materials Methods (2024) Vol. 4, Iss. 1
Open Access | Times Cited: 1
MATEO: intermolecular α-amidoalkylation theoretical enantioselectivity optimization. Online tool for selection and design of chiral catalysts and products
Paula Carracedo-Reboredo, Eider Aranzamendi, Shan He, et al.
Journal of Cheminformatics (2024) Vol. 16, Iss. 1
Open Access | Times Cited: 1
https://2DMat.ChemDX.org: Experimental data platform for 2D materials from synthesis to physical properties
Jin‐Hoon Yang, Habin Kang, Hyuk Jin Kim, et al.
Digital Discovery (2024) Vol. 3, Iss. 3, pp. 573-585
Open Access | Times Cited: 1
Computational Modeling of CO 2 Reduction and Conversion via Heterogeneous and Homogeneous Catalysis
Yue Zhang, Lin Zhang, Denghui Ma, et al.
(2024), pp. 335-360
Closed Access | Times Cited: 1
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