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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:

Comment on ‘Physics-based representations for machine learning properties of chemical reactions’
Kevin Spiekermann, Thijs Stuyver, Lagnajit Pattanaik, et al.
Machine Learning Science and Technology (2023) Vol. 4, Iss. 4, pp. 048001-048001
Open Access | Times Cited: 10

Showing 10 citing articles:

When Do Quantum Mechanical Descriptors Help Graph Neural Networks to Predict Chemical Properties?
Shih‐Cheng Li, Haoyang Wu, Angiras Menon, et al.
Journal of the American Chemical Society (2024) Vol. 146, Iss. 33, pp. 23103-23120
Closed Access | Times Cited: 7
Graph to Activation Energy Models Easily Reach Irreducible Errors but Show Limited Transferability
Sai Mahit Vadaddi, Qiyuan Zhao, Brett M. Savoie
The Journal of Physical Chemistry A (2024) Vol. 128, Iss. 13, pp. 2543-2555
Closed Access | Times Cited: 5
Subgraph Isomorphic Decision Tree to Predict Radical Thermochemistry with Bounded Uncertainty Estimation
Hao‐Wei Pang, Xiaorui Dong, Matthew S. Johnson, et al.
The Journal of Physical Chemistry A (2024) Vol. 128, Iss. 14, pp. 2891-2907
Closed Access | Times Cited: 5
Graph-Based Deep Learning Models for Thermodynamic Property Prediction: The Interplay between Target Definition, Data Distribution, Featurization, and Model Architecture
Bowen Deng, Thijs Stuyver
Journal of Chemical Information and Modeling (2025)
Closed Access
A Subgraph Isomorphic Decision Tree to Predict Radical Thermochemistry with Bounded Uncertainty Estimation
Hao‐Wei Pang, Xiaorui Dong, Matthew S. Johnson, et al.
(2024)
Open Access | Times Cited: 2
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
Geometric deep learning for molecular property predictions with chemical accuracy across chemical space
Maarten R. Dobbelaere, István Lengyel, Christian V. Stevens, et al.
Journal of Cheminformatics (2024) Vol. 16, Iss. 1
Open Access | Times Cited: 1
Machine Learning Validation via Rational Dataset Sampling with astartes
Jackson Burns, Kevin Spiekermann, Himaghna Bhattacharjee, et al.
The Journal of Open Source Software (2023) Vol. 8, Iss. 91, pp. 5996-5996
Open Access | Times Cited: 4
Graph to Activation Energy Models Easily Reach Irreducible Errors but Show Limited Transferability
Sai Mahit Vadaddi, Qiyuan Zhao, Brett M. Savoie
(2023)
Open Access | Times Cited: 3
Accurately Predicting Barrier Heights for Radical Reactions in Solution Using Deep Graph Networks
Kevin Spiekermann, Xiaorui Dong, Angiras Menon, et al.
The Journal of Physical Chemistry A (2024)
Closed Access
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