Open Access Helper

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:

Predicting Solubility Limits of Organic Solutes for a Wide Range of Solvents and Temperatures
Florence H. Vermeire, Yunsie Chung, William H. Green
(2022)
Open Access | Times Cited: 13

Showing 13 citing articles:

Chemprop: A Machine Learning Package for Chemical Property Prediction
Esther Heid, Kevin P. Greenman, Yunsie Chung, et al.
(2023)
Open Access | Times Cited: 9
Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates
Yunsie Chung, William H. Green
(2023)
Open Access | Times Cited: 5
PythiaCHEM : a user-friendly machine learning toolkit for chemistry
Stamatia Zavitsanou, Zonghua Bo, Emanuele Casali, et al.
(2024)
Open Access | Times Cited: 1
Machine learning for catalyst design: data matters
Pedro S.F. Mendes, Florence H. Vermeire, Thibaut Van Haute, et al.
Research Square (Research Square) (2024)
Open Access | Times Cited: 1
When Do Quantum Mechanical Descriptors Help Graph Neural Networks Predict Chemical Properties?
Shih‐Cheng Li, Haoyang Wu, Angiras Menon, et al.
(2024)
Open Access | Times Cited: 1
Perspective on Automated Predictive Kinetics using Estimates derived from Large Datasets
William H. Green
(2024)
Open Access | Times Cited: 1
Exploring the Solubility Limits of Edaravone in Neat Solvents and Binary Mixtures: Experimental and Machine Learning Study
Maciej Przybyłek, Tomasz Jeliński, Magdalena Mianowana, et al.
(2023)
Open Access | Times Cited: 3
Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates
Yunsie Chung, William H. Green
(2024)
Open Access
When Do Quantum Mechanical Descriptors Help Graph Neural Networks Predict Chemical Properties?
Shih‐Cheng Li, Haoyang Wu, Angiras Menon, et al.
(2024)
Open Access
Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates
Yunsie Chung, William H. Green
(2023)
Open Access | Times Cited: 1
Designing solvent systems in chemical processes using self-evolving solubility databases and graph neural networks
Yeonjoon Kim, Hojin Jung, Sabari Kumar, et al.
(2023)
Open 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
Designing solvent systems in chemical processes using self-evolving solubility databases and graph neural networks
Yeonjoon Kim, Hojin Jung, Sabari Kumar, et al.
(2022)
Open Access
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