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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:

Artificial intelligence-driven design of fuel mixtures
Nursulu Kuzhagaliyeva, Samuel Horváth, John A. Williams, et al.
Communications Chemistry (2022) Vol. 5, Iss. 1
Open Access | Times Cited: 20

Showing 20 citing articles:

Sustainable Aviation Fuels for Clean Skies: Exploring the Potential and Perspectives of Strained Hydrocarbons
Feng Wang, Dilip Rijal
Energy & Fuels (2024) Vol. 38, Iss. 6, pp. 4904-4920
Open Access | Times Cited: 15
Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview
André Nicolle, Sili Deng, Matthias Ihme, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 3, pp. 597-620
Closed Access | Times Cited: 6
Artificial Intelligence-Driven Modeling for Hydrogel Three-Dimensional Printing: Computational and Experimental Cases of Study
Harbil Bediaga, Isabel Moreno, Sonia Arrasate, et al.
Polymers (2025) Vol. 17, Iss. 1, pp. 121-121
Open Access
Software Package for Optimization of Burner Devices on Dispersed Working Fluids
Ruslan V. Fedorov, I.I. Shepelev, Mariia A. Malyoshina, et al.
Energies (2025) Vol. 18, Iss. 4, pp. 806-806
Open Access
Molecular Design of Fuels for Maximum Spark-Ignition Engine Efficiency by Combining Predictive Thermodynamics and Machine Learning
Lorenz Fleitmann, Philipp Ackermann, Johannes Schilling, et al.
Energy & Fuels (2023) Vol. 37, Iss. 3, pp. 2213-2229
Open Access | Times Cited: 12
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
Uncertainty quantification of a deep learning fuel property prediction model
Kiran K. Yalamanchi, Sahil Kommalapati, Pinaki Pal, et al.
Applications in Energy and Combustion Science (2023) Vol. 16, pp. 100211-100211
Open Access | Times Cited: 6
Machine Learning for Fuel Property Predictions: A Multi-Task and Transfer Learning Approach
Tara Larsson, Florence H. Vermeire, Sebastian Verhelst
SAE technical papers on CD-ROM/SAE technical paper series (2023)
Open Access | Times Cited: 5
Leveraging High-throughput Molecular Simulations and Machine Learning for Formulation Design
Alex K. Chew, Mohammad Atif Faiz Afzal, Zach Kaplan, et al.
(2024)
Open Access | Times Cited: 1
CALiSol-23: Experimental electrolyte conductivity data for various Li-salts and solvent combinations
Paolo de Blasio, Jonas Elsborg, Tejs Vegge, et al.
Scientific Data (2024) Vol. 11, Iss. 1
Open Access | Times Cited: 1
Machine learning-driven screening of fuel additives for increased spark-ignition engine efficiency
Shashank S. Nagaraja, S. Mani Sarathy, Balaji Mohan, et al.
Proceedings of the Combustion Institute (2024) Vol. 40, Iss. 1-4, pp. 105658-105658
Closed Access | Times Cited: 1
Prediction of Yield Sooting Index Utilizing Artificial Neural Networks and Adaptive-Network-Based Fuzzy Inference Systems
Faisal Alboqami, Amjad Ali Pasha, Mohammad Irfan Alam, et al.
Arabian Journal for Science and Engineering (2022) Vol. 48, Iss. 7, pp. 8901-8909
Closed Access | Times Cited: 4
Artificial Intelligence in Diesel Engines
Hasan Köten, Mohammad Mostafa Namar
IntechOpen eBooks (2024)
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
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
Machine learning applications for thermochemical and kinetic property prediction
Lowie Tomme, Yannick Ureel, Maarten R. Dobbelaere, et al.
Reviews in Chemical Engineering (2024)
Open Access
Prediction of Emission Characteristics of Spark Ignition (S.I.) Engines with Premium Level Gasoline-Ethanol-Alkane Blends Using Machine Learning
Sujit Kumbhar, Sanjay Khot, Varsha Jujare, et al.
Communications in computer and information science (2024), pp. 144-168
Closed Access
Learning Molecular Mixture Property Using Chemistry-Aware Graph Neural Network
Hengrui Zhang, Tianxing Lai, Jie Chen, et al.
PRX Energy (2024) Vol. 3, Iss. 2
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
MolSets: Molecular Graph Deep Sets Learning for Mixture Property Modeling
Hengrui Zhang, Jie Chen, James M. Rondinelli, et al.
arXiv (Cornell University) (2023)
Open Access
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