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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:

Synthetic data enable experiments in atomistic machine learning
John L. A. Gardner, Zoé Faure Beaulieu, Volker L. Deringer
Digital Discovery (2023) Vol. 2, Iss. 3, pp. 651-662
Open Access | Times Cited: 10

Showing 10 citing articles:

Robustness of Local Predictions in Atomistic Machine Learning Models
Sanggyu Chong, Federico Grasselli, Chiheb Ben Mahmoud, et al.
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 22, pp. 8020-8031
Open Access | Times Cited: 16
Synthetic pre-training for neural-network interatomic potentials
John L. A. Gardner, Kathryn T. Baker, Volker L. Deringer
Machine Learning Science and Technology (2023) Vol. 5, Iss. 1, pp. 015003-015003
Open Access | Times Cited: 13
ColabFit exchange: Open-access datasets for data-driven interatomic potentials
Joshua A. Vita, Eric G. Fuemmeler, Amit Gupta, et al.
The Journal of Chemical Physics (2023) Vol. 159, Iss. 15
Open Access | Times Cited: 7
Supramolecular Chemistry: Exploring the Use of Electronic Structure, Molecular Dynamics, and Machine Learning Approaches
Matheus Cachoeira Colaço, Vinícius A. Glitz, A. Jacobs, et al.
European Journal of Organic Chemistry (2024) Vol. 27, Iss. 27
Closed Access | Times Cited: 1
Transfer learning for accurate description of atomic transport in Al–Cu melts
E. O. Khazieva, N. M. Chtchelkatchev, R. E. Ryltsev
The Journal of Chemical Physics (2024) Vol. 161, Iss. 17
Closed Access | Times Cited: 1
Coarse-grained versus fully atomistic machine learning for zeolitic imidazolate frameworks
Zoé Faure Beaulieu, Thomas C. Nicholas, John L. A. Gardner, et al.
Chemical Communications (2023) Vol. 59, Iss. 76, pp. 11405-11408
Open Access | Times Cited: 3
Can I trust my fake data -- A comprehensive quality assessment framework for synthetic tabular data in healthcare
Vibeke Binz Vallevik, Aleksandar Babić, Serena Marshall, et al.
arXiv (Cornell University) (2024)
Open Access
Revisiting the Application of Machine Learning Approaches in Predicting Aqueous Solubility
Tianyuan Zheng, John B. O. Mitchell, Simon Dobson
ACS Omega (2024) Vol. 9, Iss. 32, pp. 35209-35222
Open Access
Prediction rigidities for data-driven chemistry
Sanggyu Chong, Filippo Bigi, Federico Grasselli, et al.
Faraday Discussions (2024)
Open Access
Robustness of Local Predictions in Atomistic Machine Learning Models
Sanggyu Chong, Federico Grasselli, Chiheb Ben Mahmoud, et al.
arXiv (Cornell University) (2023)
Open Access
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