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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:

Physics-informed graph neural networks for predicting cetane number with systematic data quality analysis
Yeonjoon Kim, Jaeyoung Cho, Nimal Naser, et al.
Proceedings of the Combustion Institute (2022) Vol. 39, Iss. 4, pp. 4969-4978
Open Access | Times Cited: 22

Showing 22 citing articles:

Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects
Van Nhanh Nguyen, W. Tarełko, Prabhakar Sharma, et al.
Energy & Fuels (2024) Vol. 38, Iss. 3, pp. 1692-1712
Closed Access | Times Cited: 39
Recent advances and future prospects of thermochemical biofuel conversion processes with machine learning
Pil Rip Jeon, Jong-Ho Moon, Nafiu Olanrewaju Ogunsola, et al.
Chemical Engineering Journal (2023) Vol. 471, pp. 144503-144503
Open Access | Times Cited: 38
Ignition delay prediction for fuels with diverse molecular structures using transfer learning-based neural networks
Yang Mo, Dezhi Zhou
Energy and AI (2025), pp. 100467-100467
Open Access
Descriptors-based machine-learning prediction of cetane number using quantitative structure–property relationship
Rodolfo S. M. Freitas, Xi Jiang
Energy and AI (2024) Vol. 17, pp. 100385-100385
Open Access | Times Cited: 2
Artificial intelligence for novel fuel design
S. Mani Sarathy, Basem A. Eraqi
Proceedings of the Combustion Institute (2024) Vol. 40, Iss. 1-4, pp. 105630-105630
Closed Access | Times Cited: 2
Numerical Approaches to Determine Cetane Number of Hydrocarbons and Oxygenated Compounds, Mixtures, and their Blends
Benoît Creton, Nathalie Brassart, Amandine Herbaut, et al.
Energy & Fuels (2024) Vol. 38, Iss. 16, pp. 15652-15661
Closed Access | Times Cited: 2
Optimized synthetic data and semi-supervised learning for Derived Cetane Number prediction
Manaf Sheyyab, Patrick Lynch, Eric Mayhew, et al.
Combustion and Flame (2023) Vol. 259, pp. 113184-113184
Closed Access | Times Cited: 6
Designing solvent systems using self-evolving solubility databases and graph neural networks
Yeonjoon Kim, Hojin Jung, Sabari Kumar, et al.
Chemical Science (2023) Vol. 15, Iss. 3, pp. 923-939
Open Access | Times Cited: 5
Artificial intelligence as a catalyst for combustion science and engineering
Matthias Ihme, Wai Tong Chung
Proceedings of the Combustion Institute (2024) Vol. 40, Iss. 1-4, pp. 105730-105730
Closed Access | Times Cited: 1
Physics-Informed Neural Networks with Group Contribution Methods
Mohammad Reza Babaei, Ryan Stone, Thomas A. Knotts, et al.
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 13, pp. 4163-4171
Closed Access | Times Cited: 4
Kinetic model-based group contribution method for derived cetane number prediction of oxygenated fuel components and blends
Dustin Witkowski, Michael Groendyk, David Rothamer
Combustion and Flame (2023) Vol. 255, pp. 112883-112883
Open Access | Times Cited: 4
Characterizing Interconnection Networks in Terms of Complexity via Entropy Measures
Jinhong Zhang, Asfand Fahad, Muzammil Mukhtar, et al.
Symmetry (2023) Vol. 15, Iss. 10, pp. 1868-1868
Open Access | Times Cited: 3
Fuel Ignition Delay Maps for Molecularly Controlled Combustion
Marcel Neumann, Jan G. Rittig, Ahmed Ben Letaief, et al.
Energy & Fuels (2024) Vol. 38, Iss. 14, pp. 13264-13277
Closed Access
Design Green Chemicals by Predicting Vaporization Properties Using Explainable Graph Attention Networks
Yeonjoon Kim, Jae‐Young Cho, Hojin Jung, et al.
Green Chemistry (2024) Vol. 26, Iss. 19, pp. 10247-10264
Open Access
Sooting tendencies: Combustion science for designing sustainable fuels with improved properties
Lisa D. Pfefferle, Seonah Kim, Sabari Kumar, et al.
Proceedings of the Combustion Institute (2024) Vol. 40, Iss. 1-4, pp. 105750-105750
Closed Access
Estimation of cetane number using machine learning
Balaji Mohan, Abdullah S. AlRamadan
Fuel (2024) Vol. 381, pp. 133462-133462
Closed Access
A Multimodal Learning Model based on a QSPR approach for the estimation of RON, MON and CN, for any C, H, O hydrocarbons
Roda Bounaceur, Nicolas Barthélemy, Nicolas Delort, et al.
Fuel (2024) Vol. 381, pp. 133438-133438
Open Access
A Multimodal Learning Model Based on a Qspr Approach for the Estimation of Ron, Mon and Cn, for Any C, H, O Hydrocarbons
Roda Bounaceur, Nicolas Barthélemy, Nicolas Delort, et al.
(2024)
Closed Access
Determination of Cetane Numbers Via Chemical Kinetic Mechanism
Marleen Schmidt, Samuel Schlichting, Jens Melder, et al.
Journal of Engineering for Gas Turbines and Power (2023) Vol. 146, Iss. 2
Open Access | Times Cited: 1
Interconnection network analysis through ve-degree-based information functional entropy and complexity
Wenhu Wang, Asfand Fahad, Mariano Vladimir, et al.
The European Physical Journal Plus (2023) Vol. 138, Iss. 12
Closed Access | Times Cited: 1
Designing solvent systems in chemical processes using self-evolving solubility databases and graph neural networks
Yeonjoon Kim, Hojin Jung, Sabari Kumar, et al.
(2023)
Open Access
Designing solvent systems in chemical processes using self-evolving solubility databases and graph neural networks
Yeonjoon Kim, Hojin Jung, Sabari Kumar, et al.
(2022)
Open Access
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