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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‐Driven Machine Learning for Understanding Surface Structures of Heterogeneous Catalysts
Haobo Li, Yan Jiao, Kenneth Davey, et al.
Angewandte Chemie International Edition (2022) Vol. 62, Iss. 9
Open Access | Times Cited: 69

Showing 51-75 of 69 citing articles:

Machine-Learning Accelerated DFT to Predict Activation Energies on Amorphous Nanocatalysts for the Chlorine Evolution Reaction
Xi Zhang, Haitao Chen, Kangpu Li, et al.
ACS Applied Nano Materials (2024) Vol. 7, Iss. 12, pp. 14288-14296
Closed Access | Times Cited: 2
How scanning probe microscopy can be supported by artificial intelligence and quantum computing?
Agnieszka Pręgowska, Agata Roszkiewicz, Magdalena Osial, et al.
Microscopy Research and Technique (2024)
Open Access | Times Cited: 2
Machine learning-driven catalyst design, synthesis and performance prediction for CO2 hydrogenation
Muhammad Asif, Chengxi Yao, Zitu Zuo, et al.
Journal of Industrial and Engineering Chemistry (2024) Vol. 144, pp. 32-47
Closed Access | Times Cited: 2
Application of graph neural network in computational heterogeneous catalysis
Zihao Jiao, Ya Liu, Ziyun Wang
The Journal of Chemical Physics (2024) Vol. 161, Iss. 17
Closed Access | Times Cited: 2
Gold nanoparticles supported on modified dumbbell shaped nanorod cluster CuBi2O4 for the selective oxidation of benzyl alcohol under visible light
Xueyi Duan, Siqinhaolu Hao, Tinglan Wang, et al.
Journal of Photochemistry and Photobiology A Chemistry (2023) Vol. 447, pp. 115198-115198
Closed Access | Times Cited: 6
Machine Learning Confirms the Formation Mechanism of a Single-Atom Catalyst via Infrared Spectroscopic Analysis
Yanzhang Zhao, Huan Li, Jieqiong Shan, et al.
The Journal of Physical Chemistry Letters (2023) Vol. 14, Iss. 49, pp. 11058-11062
Closed Access | Times Cited: 6
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
Modeling Realistic Structures of Trimetallic Nanoalloy Catalysts Using Chemically Meaningful Descriptors
Arravind Subramanian, Mikhail V. Polynski, Mathan K. Eswaran, et al.
(2024)
Open Access | Times Cited: 1
Machine Learning‐Driven Selection of Two‐Dimensional Carbon‐Based Supports for Dual‐Atom Catalysts in CO2 Electroreduction
Zhen Tan, Xinyu Li, Yanzhang Zhao, et al.
ChemCatChem (2024) Vol. 16, Iss. 22
Open Access | Times Cited: 1
Elucidating atomic structure and reconstruction of Mn3O4(0 0 1) surface
Shengdi Zhao, Yun Liu, Youbin Wu, et al.
Applied Surface Science (2024), pp. 161339-161339
Closed Access | Times Cited: 1
Toward Interpretable Machine Learning Models for Predicting Spectroscopy, Catalysis, and Reactions
Jun Jiang, Shubin Liu
(2024), pp. 481-512
Closed Access | Times Cited: 1
Computational Chemistry and Machine Learning-Assisted Screening of Supported Amorphous Metal Oxide Nanoclusters for Methane Activation
Xijun Wang, Kaihang Shi, Anyang Peng, et al.
ACS Catalysis (2024), pp. 18708-18721
Closed Access | Times Cited: 1
Accessing complex reconstructed material structures with hybrid global optimization acceleratedviaon-the-fly machine learning
Xiangcheng Shi, Dongfang Cheng, Ran Zhao, et al.
Chemical Science (2023) Vol. 14, Iss. 33, pp. 8777-8784
Open Access | Times Cited: 3
Metal-based electrocatalysts with data-driven designed particle size for hydrogen evolution
Chang Liu, Shan Zhu, Fengxiang Sun, et al.
Chemical Engineering Journal (2023) Vol. 476, pp. 146918-146918
Closed Access | Times Cited: 2
Machine learning-based design of pincer catalysts for polymerization reaction
Shrabani Dinda, Tanvi Bhola, Suyash Pant, et al.
Journal of Catalysis (2024), pp. 115766-115766
Closed Access
DFT Investigation of X55 (X = Ni, Pd, and Pt) Clusters on Ultrathin Supported MgO Films: Evidence of Oxygen Spillover and Relevance for Catalytic Model Studies
Mirali Jahangirzadeh Varjovi, Sergio Tosoni
The Journal of Physical Chemistry C (2024) Vol. 128, Iss. 50, pp. 21331-21342
Closed Access
Establishing C-C electro-coupling mechanism via big data analyses of reaction networks
Shi Zhang Qiao, Haobo Li, Xinyu Li, et al.
Research Square (Research Square) (2024)
Open Access
Data-Driven Discovery of Reaction Pathways for Modeling Catalytic Cracking of Hydrocarbons
Elizabeth R. Belden, Avery Brown, Randy Paffenroth, et al.
Industrial & Engineering Chemistry Research (2024) Vol. 63, Iss. 42, pp. 17889-17900
Closed Access
Unbiased dataset for methane dry reforming and catalyst design guidelines obtained by high-throughput experimentation and machine learning
Wentao Du, Patchanee Chammingkwan, Keisuke Takahashi, et al.
Journal of Catalysis (2024), pp. 115930-115930
Closed Access
Single‐Atom Heterogeneous Catalysts: Human‐ and AI‐Driven Platform for Augmented Designs, Analytics and Reality‐Enabled Manufacturing
Lei Cheng, Yawen Tang, Kostya Ostrikov, et al.
Angewandte Chemie (2023) Vol. 136, Iss. 5
Open Access
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