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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:

Gibbs–Helmholtz graph neural network: capturing the temperature dependency of activity coefficients at infinite dilution
Edgar Iván Sánchez Medina, Steffen Linke, Martin Stoll, et al.
Digital Discovery (2023) Vol. 2, Iss. 3, pp. 781-798
Open Access | Times Cited: 18

Showing 18 citing articles:

Physical pooling functions in graph neural networks for molecular property prediction
Artur M. Schweidtmann, Jan G. Rittig, Jana M. Weber, et al.
Computers & Chemical Engineering (2023) Vol. 172, pp. 108202-108202
Open Access | Times Cited: 33
Gibbs–Duhem-informed neural networks for binary activity coefficient prediction
Jan G. Rittig, Kobi Felton, Alexei A. Lapkin, et al.
Digital Discovery (2023) Vol. 2, Iss. 6, pp. 1752-1767
Open Access | Times Cited: 21
ML-SAFT: A machine learning framework for PCP-SAFT parameter prediction
Kobi Felton, Lukas Raßpe-Lange, Jan G. Rittig, et al.
Chemical Engineering Journal (2024) Vol. 492, pp. 151999-151999
Open Access | Times Cited: 8
Thermodynamics-consistent graph neural networks
Jan G. Rittig, Alexander Mitsos
Chemical Science (2024)
Open Access | Times Cited: 7
Predicting the temperature-dependent CMC of surfactant mixtures with graph neural networks
Christoforos Brozos, Jan G. Rittig, Elie Akanny, et al.
Computers & Chemical Engineering (2025), pp. 109085-109085
Open Access
Similarity-Informed Matrix Completion Method for Predicting Activity Coefficients
Nicolas Hayer, Thomas Specht, Justus Arweiler, et al.
The Journal of Physical Chemistry A (2025)
Closed Access
Pooling solvent mixtures for solvation free energy predictions
Roel J. Leenhouts, Nathan Morgan, Emad Al Ibrahim, et al.
Chemical Engineering Journal (2025), pp. 162232-162232
Open Access
GRAPPA—A hybrid graph neural network for predicting pure component vapor pressures
Marco Hoffmann, Hans Hasse, Fabian Jirasek
Chemical Engineering Journal Advances (2025), pp. 100750-100750
Open Access
Review on Graph Neural Networks for Process Soft Sensor Development, Fault Diagnosis, and Process Monitoring
Mingwei Jia, Yuan Yao, Yi Liu
Industrial & Engineering Chemistry Research (2025)
Closed Access
An Interpretable Solute–Solvent Interactive Attention Module Intensified Graph-Learning Architecture toward Enhancing the Prediction Accuracy of an Infinite Dilution Activity Coefficient
Di Wu, Zutao Zhu, Jun Zhang, et al.
Industrial & Engineering Chemistry Research (2024) Vol. 63, Iss. 19, pp. 8741-8750
Closed Access | Times Cited: 3
Multi-fidelity graph neural networks for predicting toluene/water partition coefficients
Thomas Nevolianis, Jan G. Rittig, Alexander Mitsos, et al.
(2024)
Open Access | Times Cited: 3
Graph neural networks for CO2 solubility predictions in Deep Eutectic Solvents
Gabriel Hernández-Morales, Edgar Iván Sánchez Medina, Arturo Jiménez‐Gutiérrez, et al.
Computers & Chemical Engineering (2024) Vol. 187, pp. 108750-108750
Closed Access | Times Cited: 2
Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks
Christoforos Brozos, Jan G. Rittig, Sandip Bhattacharya, et al.
Journal of Chemical Theory and Computation (2024) Vol. 20, Iss. 13, pp. 5695-5707
Open Access | Times Cited: 2
Gibbs–Helmholtz Graph Neural Network for the Prediction of Activity Coefficients of Polymer Solutions at Infinite Dilution
Edgar Iván Sánchez Medina, Sreekanth Kunchapu, Kai Sundmacher
The Journal of Physical Chemistry A (2023) Vol. 127, Iss. 46, pp. 9863-9873
Open Access | Times Cited: 6
Machine Learning-Supported Solvent Design for Lignin-First Biorefineries and Lignin Upgrading
Laura König‐Mattern, Edgar Sanchez Medina, Anastasia O. Komarova, et al.
(2024)
Closed Access
A symbolic regression based methodology for the construction of interpretable and predictive thermodynamic models
Sam Kay, Edgar Iván Sánchez Medina, Kai Sundmacher, et al.
Computer-aided chemical engineering/Computer aided chemical engineering (2024), pp. 2701-2706
Closed Access
Learning Hybrid Extraction and Distillation using Phenomena-based String Representation
Jianping Li
Systems and Control Transactions (2024) Vol. 3, pp. 300-307
Closed Access
Gibbs-Duhem-Informed Neural Networks for Binary Activity Coefficient Prediction
Jan G. Rittig, Kobi Felton, Alexei A. Lapkin, et al.
arXiv (Cornell University) (2023)
Open Access
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