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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–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: 15

Showing 15 citing articles:

Graph neural networks for surfactant multi-property prediction
Christoforos Brozos, Jan G. Rittig, Sandip Bhattacharya, et al.
Colloids and Surfaces A Physicochemical and Engineering Aspects (2024) Vol. 694, pp. 134133-134133
Open Access | Times Cited: 6
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: 4
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
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: 2
HANNA: Hard-constraint Neural Network for Consistent Activity Coefficient Prediction
Thomas Specht, Mayank Nagda, Sophie Fellenz, et al.
Chemical Science (2024) Vol. 15, Iss. 47, pp. 19777-19786
Open Access | Times Cited: 2
HybridGamma: A thermodynamically consistent framework for hybrid modelling of activity coefficients
Ulderico Di Caprio, Jan Degrève, Peter Hellinckx, et al.
Chemical Engineering Journal (2023) Vol. 475, pp. 146104-146104
Open Access | Times Cited: 5
PUFFIN: A path-unifying feed-forward interfaced network for vapor pressure prediction
Vinícius V. Santana, Carine M. Rebello, Luana P. Queiroz, et al.
Chemical Engineering Science (2023) Vol. 286, pp. 119623-119623
Open Access | Times Cited: 5
Machine learning for determination of activity of water and activity coefficients of electrolytes in binary solutions
Guillaume Zante
Artificial Intelligence Chemistry (2024) Vol. 2, Iss. 1, pp. 100069-100069
Open Access | Times Cited: 1
Thermodynamics-consistent graph neural networks
Jan G. Rittig, Alexander Mitsos
Chemical Science (2024)
Open Access | Times Cited: 1
Graph Neural Networks for Surfactant Multi-Property Prediction
Christoforos Brozos, Jan G. Rittig, Sandip Bhattacharya, et al.
arXiv (Cornell University) (2024)
Open Access
Machine Learning-Aided Process Design for Microwave-Assisted Ammonia Production
Md Abdullah Al Masud, Alazar Araia, Yuxin Wang, et al.
Systems and Control Transactions (2024) Vol. 3, pp. 316-321
Closed Access
Machine Learning Models for Vapor-Liquid Equilibrium of Binary Mixtures: State of the Art and Future Opportunities
Gabriel Y. Ottaiano, Tiago Dias Martins
Process Safety and Environmental Protection (2024)
Closed Access
Machine learning‐aided process design using limited experimental data: A microwave‐assisted ammonia synthesis case study
Md Abdullah Al Masud, Alazar Araia, Yuxin Wang, et al.
AIChE Journal (2024)
Open Access
Development of a Helmholtz free energy equation of state for fluid and solid phases via artificial neural networks
Gustavo Chaparro, Erich A. Müller
Communications Physics (2024) Vol. 7, Iss. 1
Open Access
Contents list

Digital Discovery (2023) Vol. 2, Iss. 6, pp. 1635-1643
Open Access
Page 1

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