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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:

A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing
Benedikt Winter, Clemens Winter, Johannes Schilling, et al.
Digital Discovery (2022) Vol. 1, Iss. 6, pp. 859-869
Open Access | Times Cited: 55

Showing 1-25 of 55 citing articles:

Machine Learning Methods for Small Data Challenges in Molecular Science
Bozheng Dou, Zailiang Zhu, Ekaterina Merkurjev, et al.
Chemical Reviews (2023) Vol. 123, Iss. 13, pp. 8736-8780
Open Access | Times Cited: 181
Chemprop: A Machine Learning Package for Chemical Property Prediction
Esther Heid, Kevin P. Greenman, Yunsie Chung, et al.
Journal of Chemical Information and Modeling (2023) Vol. 64, Iss. 1, pp. 9-17
Open Access | Times Cited: 163
Leveraging large language models for predictive chemistry
Kevin Maik Jablonka, Philippe Schwaller, Andres Ortega‐Guerrero, et al.
Nature Machine Intelligence (2024) Vol. 6, Iss. 2, pp. 161-169
Open Access | Times Cited: 112
Graph neural networks for temperature-dependent activity coefficient prediction of solutes in ionic liquids
Jan G. Rittig, Karim Ben Hicham, Artur M. Schweidtmann, et al.
Computers & Chemical Engineering (2023) Vol. 171, pp. 108153-108153
Open Access | Times Cited: 46
SPT-NRTL: A physics-guided machine learning model to predict thermodynamically consistent activity coefficients
Benedikt Winter, Clemens Winter, Timm Esper, et al.
Fluid Phase Equilibria (2023) Vol. 568, pp. 113731-113731
Open Access | Times Cited: 34
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
Is GPT-3 all you need for low-data discovery in chemistry?
Kevin Maik Jablonka, Philippe Schwaller, Andres Ortega‐Guerrero, et al.
(2023)
Open Access | Times Cited: 25
Generalizing property prediction of ionic liquids from limited labeled data: a one-stop framework empowered by transfer learning
Guzhong Chen, Zhen Song, Zhiwen Qi, et al.
Digital Discovery (2023) Vol. 2, Iss. 3, pp. 591-601
Open Access | Times Cited: 23
A Review of Large Language Models and Autonomous Agents in Chemistry
Mayk Caldas Ramos, Christopher J. Collison, Andrew Dickson White
Chemical Science (2024)
Open Access | Times Cited: 13
Understanding the language of molecules: predicting pure component parameters for the PC-SAFT equation of state from SMILES
Benedikt Winter, Philipp Rehner, Timm Esper, et al.
Digital Discovery (2025)
Open Access | Times Cited: 1
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
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
Leveraging Large Language Models for Predictive Chemistry
Kevin Maik Jablonka, Philippe Schwaller, Andres Ortega‐Guerrero, et al.
(2023)
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
Thermodynamics-consistent graph neural networks
Jan G. Rittig, Alexander Mitsos
Chemical Science (2024)
Open Access | Times Cited: 7
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: 7
Predicting solvation free energies for neutral molecules in any solvent with openCOSMO-RS
Simon Müller, Thomas Nevolianis, Miquel García‐Ratés, et al.
Fluid Phase Equilibria (2024), pp. 114250-114250
Open Access | Times Cited: 6
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: 13
PointGAT: A Quantum Chemical Property Prediction Model Integrating Graph Attention and 3D Geometry
Rong Zhang, Rongqing Yuan, Boxue Tian
Journal of Chemical Theory and Computation (2024) Vol. 20, Iss. 10, pp. 4115-4128
Open Access | Times Cited: 5
Working fluid and system optimisation of organic Rankine cycles via computer-aided molecular design: A review
Christos N. Markides, André Bardow, Michel De Paepe, et al.
Progress in Energy and Combustion Science (2024) Vol. 107, pp. 101201-101201
Open Access | Times Cited: 4
A drug recommendation system based on response prediction: Integrating gene expression and K-mer fragmentation of drug SMILES using LightGBM
Sajid Naveed, Mujtaba Husnain
Intelligence-Based Medicine (2025), pp. 100206-100206
Open Access
Foundation models for materials discovery – current state and future directions
Edward O. Pyzer‐Knapp, Matteo Manica, Peter Staar, et al.
npj Computational Materials (2025) Vol. 11, Iss. 1
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
Hierarchical matrix completion for the prediction of properties of binary mixtures
Dominik Gond, Jan‐Tobias Sohns, Heike Leitte, et al.
Computers & Chemical Engineering (2025), pp. 109122-109122
Open Access
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