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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:

Machine-Learning Energy Gaps of Porphyrins with Molecular Graph Representations
Zheng Li, Noushin Omidvar, Wei Shan Chin, et al.
The Journal of Physical Chemistry A (2018) Vol. 122, Iss. 18, pp. 4571-4578
Closed Access | Times Cited: 57

Showing 26-50 of 57 citing articles:

Machine Learning Predictor Models in the Electronic Properties of Alkanes based on Degree-Topology Indices
Zubainun Mohamed Zabidi, Ahmad Nazib Alias, Nurul Aimi Zakaria, et al.
International Journal of Emerging Technology and Advanced Engineering (2021) Vol. 11, Iss. 11, pp. 1-14
Open Access | Times Cited: 26
Exploring the potential of in silico machine learning tools for the prediction of acute Daphnia magna nanotoxicity
Surendra Balraadjsing, Willie J.G.M. Peijnenburg, Martina G. Vijver
Chemosphere (2022) Vol. 307, pp. 135930-135930
Open Access | Times Cited: 19
MAG-SOLex Molecular Representation: A Methodology for Handling Complex Molecules in Algorithms
Diego Telles Fernandes, Karina Klock da Costa, Helton Siqueira Maciel, et al.
ACS Omega (2025) Vol. 10, Iss. 6, pp. 5645-5658
Open Access
Meso-carbazole substituted porphyrin complexes: Synthesis and spectral properties according to experiment, DFT calculations and the prediction by machine learning methods
Н. Г. Бичан, Е. Н. Овченкова, Alexander A. Ksenofontov, et al.
Dyes and Pigments (2022) Vol. 204, pp. 110470-110470
Closed Access | Times Cited: 16
Machine learning for multiscale modeling in computational molecular design
Abdulelah S. Alshehri, Fengqi You
Current Opinion in Chemical Engineering (2021) Vol. 36, pp. 100752-100752
Closed Access | Times Cited: 20
Key factors governing the device performance of CIGS solar cells: Insights from machine learning
Cheng-Wan Zhu, Wu Liu, Yaoyao Li, et al.
Solar Energy (2021) Vol. 228, pp. 45-52
Closed Access | Times Cited: 19
Development of an Accurate Coarse-Grained Model of Poly(acrylic acid) in Explicit Solvents
Yaxin An, Samrendra Singh, Karteek K. Bejagam, et al.
Macromolecules (2019) Vol. 52, Iss. 13, pp. 4875-4887
Closed Access | Times Cited: 21
A Simple and Promising Prediction Model to Analyze the Optical Properties of Organic Photovoltaic Materials
Sijing Zhong, Wenhao Hsu, Han Y. H. Chen, et al.
Solar RRL (2024) Vol. 8, Iss. 12
Closed Access | Times Cited: 2
Insights into nanoparticle toxicity against aquatic organisms using multivariate regression, Read-Across, and ML algorithms: Predictive models for Daphnia magna and Danio rerio
Joyita Roy, Kunal Roy
Aquatic Toxicology (2024) Vol. 276, pp. 107114-107114
Closed Access | Times Cited: 2
Systematic Study of the Effect of Auxiliary Acceptors in D–A′−π–A Sensitizers Used on Dye-Sensitized Solar Cells
Ping Li, Houyu Zhang, Alessandro Troisi
The Journal of Physical Chemistry C (2018) Vol. 122, Iss. 42, pp. 23890-23898
Closed Access | Times Cited: 20
Identifying Molecular Structure–Energy Level Quantitative Relationship of Thermally Activated Delayed Fluorescence Materials Using Machine Learning
Haochen Shi, Yingjie Li, Suling Zhao, et al.
The Journal of Physical Chemistry C (2023) Vol. 127, Iss. 48, pp. 23526-23535
Closed Access | Times Cited: 5
Machine Learning Frontier Orbital Energies of Nanodiamonds
Thorren Kirschbaum, Börries von Seggern, Joachim Dzubiella, et al.
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 14, pp. 4461-4473
Open Access | Times Cited: 4
Machine learning-enabled discovery of multi-resonance TADF molecules: Unraveling PLQY predictions from molecular structures
Haochen Shi, Yiming Shi, Zhiqin Liang, et al.
Chemical Engineering Journal (2024) Vol. 494, pp. 153150-153150
Closed Access | Times Cited: 1
Enumeration of de novo Inorganic Complexes for Chemical Discovery and Machine Learning
Stefan Gugler, Jon Paul Janet, Heather J. Kulik
(2019)
Open Access | Times Cited: 9
Counting Polynomials in Chemistry: Past, Present, and Perspectives
Dan-Marian Joița, Mihaela Aurelia Tomescu, Lorentz Jäntschi
Symmetry (2023) Vol. 15, Iss. 10, pp. 1815-1815
Open Access | Times Cited: 3
New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts
Aditya Nandy, Chenru Duan, Conrad Goffinet, et al.
(2022)
Open Access | Times Cited: 5
Machine learning the frontier orbital energies of SubPc based triads
Freja Eilsø Storm, Linnea M. Folkmann, Thorsten Hansen, et al.
Journal of Molecular Modeling (2022) Vol. 28, Iss. 10
Closed Access | Times Cited: 5
Efficient photocatalysts of a tetraphenylporphyrin/P25 hybrid for visible-light photoreduction of CO2
Hongyi Gao, Mengyi Jia, Siyuan Chen, et al.
New Journal of Chemistry (2020) Vol. 44, Iss. 40, pp. 17229-17235
Closed Access | Times Cited: 3
Making Machine Learning Work in Chemistry: Methodological Innovation, Software Development, and Application Studies
Mojtaba Haghighatlari
(2019)
Closed Access | Times Cited: 3
Supervised Machine Learning-Graph Theory Approach For Analyzing the Electronic Properties of Alkanes
Zubainun Mohamed Zabidi, Nurul Aimi Zakaria, Ahmad Nazib Alias
Journal of the Turkish Chemical Society Section A Chemistry (2023) Vol. 11, Iss. 1, pp. 137-148
Open Access | Times Cited: 1
5‐3: Predicting External Quantum Efficiency of Red Phosphorescent Organic Light‐Emitting Devices by Machine Learning
Lu Wang, Zhiyang Li, Liu Shuyao, et al.
SID Symposium Digest of Technical Papers (2024) Vol. 55, Iss. S1, pp. 53-55
Closed Access
The donor–acceptor dyad based on high substituted fullero[70]pyrrolidine-coordinated manganese (III) phthalocyanine for photoinduced electron transfer
Е. Н. Овченкова, Н. Г. Бичан, F. E. Gostev, et al.
Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (2021) Vol. 263, pp. 120166-120166
Closed Access | Times Cited: 3
Enumeration of de novo Inorganic Complexes for Chemical Discovery and Machine Learning
Stefan Gugler, Jon Paul Janet, Heather J. Kulik
(2019)
Open Access | Times Cited: 2
Exploring Deep Learning for Metalloporphyrins: Databases, Molecular Representations, and Model Architectures
An Su, Chengwei Zhang, Yuanbin She, et al.
(2022)
Open Access | Times Cited: 2
Exploring Deep Learning for Metalloporphyrins: Databases, Molecular Representations, and Model Architectures
An Su, Chengwei Zhang, Yuanbin She, et al.
Catalysts (2022) Vol. 12, Iss. 11, pp. 1485-1485
Open Access | Times Cited: 2

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