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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:

Capturing molecular interactions in graph neural networks: a case study in multi-component phase equilibrium
Shiyi Qin, Shengli Jiang, Jianping Li, et al.
Digital Discovery (2022) Vol. 2, Iss. 1, pp. 138-151
Open Access | Times Cited: 29

Showing 1-25 of 29 citing articles:

Computer-Aided Molecular Design of Ionic Liquids as Advanced Process Media: A Review from Fundamentals to Applications
Zhen Song, Jiahui Chen, Jie Cheng, et al.
Chemical Reviews (2023) Vol. 124, Iss. 2, pp. 248-317
Closed Access | Times Cited: 32
Counterfactual Learning on Graphs: A Survey
Zhimeng Guo, Zongyu Wu, Teng Xiao, et al.
Deleted Journal (2025) Vol. 22, Iss. 1, pp. 17-59
Open Access | Times Cited: 1
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
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
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
A comprehensive review on sustainable surfactants from CNSL: chemistry, key applications and research perspectives
Veeramanoharan Ashokkumar, Seok Chan Kim
RSC Advances (2024) Vol. 14, Iss. 35, pp. 25429-25471
Open Access | Times Cited: 8
Thermodynamics-consistent graph neural networks
Jan G. Rittig, Alexander Mitsos
Chemical Science (2024)
Open Access | Times Cited: 7
Uncertainty quantification for molecular property predictions with graph neural architecture search
Shengli Jiang, Shiyi Qin, Reid C. Van Lehn, et al.
Digital Discovery (2024) Vol. 3, Iss. 8, pp. 1534-1553
Open Access | Times Cited: 6
Classification Method for Fatigue Driving Signals Based on Multiple Classifier Analysis
Zhendong Mu
IEEJ Transactions on Electrical and Electronic Engineering (2025)
Closed Access
Molecular Feature-Based Prediction of Drug-Drug Interactions Using Graph Neural Networks
Flaviu-Ioan Gheorghita, Danut Ovidiu Pop, László Barna Iantovics
Lecture notes in networks and systems (2025), pp. 23-40
Closed Access
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
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
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 Explainable Classification Framework for Determining and Understanding the Suitability of Solvent Extraction for Bioproduct Recovery
Jianping Li, Reid C. Van Lehn, Christos T. Maravelias
ACS Sustainable Chemistry & Engineering (2024) Vol. 12, Iss. 14, pp. 5436-5446
Closed Access | Times Cited: 3
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
Predicting Temperature‐Dependent Activity Coefficients at Infinite Dilution Using Tensor Completion
Julie Damay, Gleb Ryzhakov, Fabian Jirasek, et al.
Chemie Ingenieur Technik (2023) Vol. 95, Iss. 7, pp. 1061-1069
Open Access | Times Cited: 8
Outlook: How I Learned to Love Machine Learning (A Personal Perspective on Machine Learning in Process Systems Engineering)
Ví­ctor M. Zavala
Industrial & Engineering Chemistry Research (2023) Vol. 62, Iss. 23, pp. 8995-9005
Open Access | Times Cited: 8
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
ML-SAFT: A machine learning framework for PCP-SAFT parameter prediction
Kobi Felton, Lukas Rasßpe-Lange, Jan G. Rittig, et al.
(2023)
Open Access | Times Cited: 5
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
Structural Diversity and Biological Activity of Cyanopeptolins Produced by Nostoc edaphicum CCNP1411
Robert Konkel, Marta Cegłowska, Karolina Szubert, et al.
Marine Drugs (2023) Vol. 21, Iss. 10, pp. 508-508
Open Access | Times Cited: 4
Graph Neural Network-Based Molecular Property Prediction with Patch Aggregation
Teng Jiek See, Daokun Zhang, Mario Boley, et al.
Journal of Chemical Theory and Computation (2024)
Closed Access | Times Cited: 1
Graph-Based Modeling and Molecular Dynamics for Ion Activity Coefficient Prediction in Polymeric Ion-Exchange Membranes
Pegah Naghshnejad, Gabriela Theis Marchan, Teslim Olayiwola, et al.
Industrial & Engineering Chemistry Research (2024)
Closed Access | Times Cited: 1
PlasmoData.jl — A Julia framework for modeling and analyzing complex data as graphs
David Cole, Ví­ctor M. Zavala
Computers & Chemical Engineering (2024) Vol. 185, pp. 108679-108679
Open Access
Vapor–liquid phase equilibrium prediction for mixtures of binary systems using graph neural networks
Jinke Sun, Jianfei Xue, Guangyu Yang, et al.
AIChE Journal (2024) Vol. 71, Iss. 2
Open Access
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