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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:

Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment
Sakib Matin, Alice Allen, Justin S. Smith, et al.
Journal of Chemical Theory and Computation (2024) Vol. 20, Iss. 3, pp. 1274-1281
Open Access | Times Cited: 13

Showing 13 citing articles:

Data Generation for Machine Learning Interatomic Potentials and Beyond
Maksim Kulichenko, Benjamin Nebgen, Nicholas Lubbers, et al.
Chemical Reviews (2024) Vol. 124, Iss. 24, pp. 13681-13714
Closed Access | Times Cited: 17
Refining potential energy surface through dynamical properties via differentiable molecular simulation
Bin Han, Kuang Yu
Nature Communications (2025) Vol. 16, Iss. 1
Open Access | Times Cited: 3
Accurate machine learning force fields via experimental and simulation data fusion
Sebastien Röcken, Julija Zavadlav
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 11
Numerical methods for unraveling inter-particle potentials in colloidal suspensions: A comparative study for two-dimensional suspensions
Clare R. Rees-Zimmerman, José Martín‐Roca, David Evans, et al.
The Journal of Chemical Physics (2025) Vol. 162, Iss. 7
Closed Access
Experimental evidence of quantum Drude oscillator behavior in liquids revealed with probabilistic iterative Boltzmann inversion
B. Shanks, Harry W. Sullivan, Pavel Jungwirth, et al.
The Journal of Chemical Physics (2025) Vol. 162, Iss. 16
Closed Access
A generalizable framework of solution-guided machine learning with application to nanoindentation of free-standing thin films
Ruijin Wang, Tianquan Ying, Chen Yang, et al.
Thin-Walled Structures (2024) Vol. 200, pp. 111984-111984
Closed Access | Times Cited: 3
Thermodynamic Transferability in Coarse-Grained Force Fields Using Graph Neural Networks
Emily Shinkle, Aleksandra Pachalieva, Riti Bahl, et al.
Journal of Chemical Theory and Computation (2024) Vol. 20, Iss. 23, pp. 10524-10539
Open Access | Times Cited: 2
Insight into the direct carbonation process of Ca2SiO4 based on ReaxFF MD simulation and experiments
Yajun Wang, Xiao-Pei Zhang, Dongmei Liu, et al.
Cement and Concrete Research (2024) Vol. 187, pp. 107711-107711
Closed Access | Times Cited: 1
Deciphering diffuse scattering with machine learning and the equivariant foundation model: The case of molten FeO.
Ganesh Sivaraman, Chris J. Benmore
Journal of Physics Condensed Matter (2024) Vol. 36, Iss. 38, pp. 381501-381501
Open Access
THE POINT OF OBLIVION THEOREM
Shelvin Datt
(2024)
Open Access
Correction to Density Functional Theory Calculations and Machine Learning Interatomic Potentials for Molten Salts to Achieve Experimental Accuracy
Hyun‐Seok Lee, Takuji Oda
The Journal of Physical Chemistry C (2024) Vol. 128, Iss. 46, pp. 19777-19785
Closed Access
Accurate machine learning force fields via experimental and simulation data fusion
Sebastien Röcken, Julija Zavadlav
arXiv (Cornell University) (2023)
Open Access
Accurate machine learning force fields via experimental and simulation data fusion
Julija Zavadlav, Sebastien Röcken
Research Square (Research Square) (2023)
Open Access
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