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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.

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Showing 1-25 of 53 citing articles:

Extrapolative prediction of small-data molecular property using quantum mechanics-assisted machine learning
Hajime Shimakawa, Akiko Kumada, Masahiro Sato
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 15
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
A systematic method for selecting molecular descriptors as features when training models for predicting physiochemical properties
Ana E. Comesana, Tyler Huntington, Corinne D. Scown, et al.
Fuel (2022) Vol. 321, pp. 123836-123836
Open Access | Times Cited: 36
Machine learning based technique for outlier detection and result prediction in combustion diagnostics
Mingfei Chen, Kaile Zhou, Dong Liu
Energy (2024) Vol. 290, pp. 130218-130218
Closed Access | Times Cited: 7
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
Advancing Sustainable Aviation Fuel Design: Machine Learning for High-Energy-Density Liquid Polycyclic Hydrocarbons
Dilip Rijal, V. V. Vasilyev, Feng Wang
Energy & Fuels (2025)
Closed Access
Viscosity prediction using image processing and supervised learning
Matteo Caponi, Adam B. Cox, Siddharth Misra
Fuel (2023) Vol. 339, pp. 127320-127320
Closed Access | Times Cited: 17
Exploring Machine Learning in Chemistry through the Classification of Spectra: An Undergraduate Project
Alanah Grant-St James, Luke Hand, Thomas Mills, et al.
Journal of Chemical Education (2023) Vol. 100, Iss. 3, pp. 1343-1350
Open Access | Times Cited: 14
Prediction of CO2 solubility in ionic liquids via convolutional autoencoder based on molecular structure encoding
Tianxiong Liu, Dingchao Fan, Yusen Chen, et al.
AIChE Journal (2023) Vol. 69, Iss. 10
Closed Access | Times Cited: 14
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
Chemical SuperLearner (ChemSL) - An automated machine learning framework for building physical and chemical properties model
Balaji Mohan, Junseok Chang
Chemical Engineering Science (2024) Vol. 294, pp. 120111-120111
Closed Access | Times Cited: 4
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
Prediction of hydrocarbons ignition performances using machine learning modeling
Giacomo Flora, Forood Karimzadeh, M. Kahandawala, et al.
Fuel (2024) Vol. 368, pp. 131619-131619
Closed Access | Times Cited: 3
Machine Learning for Efficient Prediction of Protein Redox Potential: The Flavoproteins Case
Bruno G. Galuzzi, Antonio Mirarchi, Edoardo Luca Viganò, et al.
Journal of Chemical Information and Modeling (2022) Vol. 62, Iss. 19, pp. 4748-4759
Open Access | Times Cited: 16
Interpretable Feedforward Neural Network and XGBoost-Based Algorithms to Predict CO2 Solubility in Ionic Liquids
Ao Yang, Shirui Sun, Hongfu Mi, et al.
Industrial & Engineering Chemistry Research (2024) Vol. 63, Iss. 18, pp. 8293-8305
Closed Access | Times Cited: 2
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
An interpretable hybrid Machine learning prediction of dielectric constant of alkali halide crystals
Jiandong Deng, Guozhu Jia
Chemical Physics (2022) Vol. 555, pp. 111457-111457
Closed Access | Times Cited: 13
Predicting the Physicochemical Properties of Molecules in Petroleum Based on Structural Increments
Lixin Hou, Lei Ye, Xinglong Qin, et al.
Industrial & Engineering Chemistry Research (2023) Vol. 62, Iss. 19, pp. 7744-7756
Closed Access | Times Cited: 7
Molecular kinematic viscosity prediction of natural ester insulating oil based on sparse Machine learning models
Hanbo Zheng, Weijie Lv, Yang Wang, et al.
Journal of Molecular Liquids (2023) Vol. 385, pp. 122355-122355
Closed Access | Times Cited: 6
Machine Learning for High Energy Density Hydrocarbons for Sustainable Aviation Fuel
Dilip Rijal, V. V. Vasilyev, Feng Wang
(2024)
Open Access | Times Cited: 1
Predicting the amino group pKa of amino acids using machine learning-QSPR methods
Mohammad-Reza Rafieezade, Ali Fazeli
Journal of Molecular Liquids (2024) Vol. 408, pp. 125355-125355
Closed Access | Times Cited: 1
Combustion condition predictions for C2-C4 alkane and alkene fuels via machine learning methods
Mingfei Chen, Jiaying He, Xuan Zhao, et al.
Fuel (2024) Vol. 373, pp. 132375-132375
Closed Access | Times Cited: 1
Compatibility analysis of waste polymer recycling in asphalt binder using molecular descriptor and graph neural network
Bingyan Cui, Hao Wang
Resources Conservation and Recycling (2024) Vol. 212, pp. 107950-107950
Open Access | Times Cited: 1
Preparation of Nanoparticle-Enriched Fuels and Prediction of Cylinder Pressure Through Machine Learning Models
Kürşat Mustafa Karaoğlan, Mehmet Çeli̇k
Arabian Journal for Science and Engineering (2024)
Closed Access | Times Cited: 1
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