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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:

Regularized by Physics: Graph Neural Network Parametrized Potentials for the Description of Intermolecular Interactions
Moritz Thürlemann, Lennard Böselt, Sereina Riniker
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 2, pp. 562-579
Open Access | Times Cited: 20

Showing 20 citing articles:

Open-Source Machine Learning in Computational Chemistry
Alexander Hagg, Karl N. Kirschner
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 15, pp. 4505-4532
Open Access | Times Cited: 28
Machine-learned molecular mechanics force fields from large-scale quantum chemical data
Kenichiro Takaba, Anika J. Friedman, Chapin E. Cavender, et al.
Chemical Science (2024) Vol. 15, Iss. 32, pp. 12861-12878
Open Access | Times Cited: 13
Learning QM/MM potential using equivariant multiscale model
Yao-Kun Lei, Kiyoshi Yagi, Yuji Sugita
The Journal of Chemical Physics (2024) Vol. 160, Iss. 21
Open Access | Times Cited: 9
Application of Modern Artificial Intelligence Techniques in the Development of Organic Molecular Force Fields
Junmin Chen, Qian Gao, Miaofei Huang, et al.
Physical Chemistry Chemical Physics (2025)
Closed Access | Times Cited: 1
Advancing Force Fields Parameterization: A Directed Graph Attention Networks Approach
Gong Chen, Théo Jaffrelot Inizan, Thomas Plé, et al.
Journal of Chemical Theory and Computation (2024) Vol. 20, Iss. 13, pp. 5558-5569
Closed Access | Times Cited: 7
Ab initio dispersion potentials based on physics-based functional forms with machine learning
Corentin Villot, Ka Un Lao
The Journal of Chemical Physics (2024) Vol. 160, Iss. 18
Closed Access | Times Cited: 5
Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond
Kenichiro Takaba, Iván Pulido, Pavan Kumar Behara, et al.
arXiv (Cornell University) (2023)
Open Access | Times Cited: 12
ABFML: A problem-oriented package for rapidly creating, screening, and optimizing new machine learning force fields
Xingze Geng, Jianing Gu, Gaowu Qin, et al.
The Journal of Chemical Physics (2025) Vol. 162, Iss. 5
Closed Access
Neural Network Potential with Multiresolution Approach Enables Accurate Prediction of Reaction Free Energies in Solution
Felix Pultar, Moritz Thürlemann, Igor Gordiy, et al.
Journal of the American Chemical Society (2025)
Open Access
End-To-End Learning of Classical Interatomic Potentials for Benchmarking Anion Polarization Effects in Lithium Polymer Electrolytes
Pablo A. Leon, Avni Singhal, Jurģis Ruža, et al.
Chemistry of Materials (2025)
Closed Access
On the design space between molecular mechanics and machine learning force fields
Yuanqing Wang, Kenichiro Takaba, Michael S. Chen, et al.
Applied Physics Reviews (2025) Vol. 12, Iss. 2
Open Access
Rapid Access to Small Molecule Conformational Ensembles in Organic Solvents Enabled by Graph Neural Network-Based Implicit Solvent Model
Paul Katzberger, Laurent Hauswirth, Antonia S. Kuhn, et al.
Journal of the American Chemical Society (2025)
Open Access
A neural network potential based on pairwise resolved atomic forces and energies
Jas Kalayan, Ismaeel Ramzan, Christopher D. Williams, et al.
Journal of Computational Chemistry (2024) Vol. 45, Iss. 14, pp. 1143-1151
Open Access | Times Cited: 3
Hybrid classical/machine-learning force fields for the accurate description of molecular condensed-phase systems
Moritz Thürlemann, Sereina Riniker
Chemical Science (2023) Vol. 14, Iss. 44, pp. 12661-12675
Open Access | Times Cited: 6
Hybrid Classical/Machine-Learning Force Fields for the Accurate Description of Molecular Condensed-Phase Systems
Moritz Thürlemann, Sereina Riniker
arXiv (Cornell University) (2023)
Open Access | Times Cited: 4
Graph Neural Network-Based Molecular Property Prediction with Patch Aggregation
Teng Jiek See, Daokun Zhang, Mario Boley, et al.
Journal of Chemical Theory and Computation (2024)
Closed Access | Times Cited: 1
Neural-Network-based Pitch-lag Estimation in Long-term Linear Prediction
Ales Jandera, Tomáš Škovránek
(2024), pp. 1-4
Closed Access
Advancing Force Fields Parameterization: A Directed Graph Attention Networks Approach
Gong Chen, Théo Jaffrelot Inizan, Thomas Plé, et al.
(2023)
Open Access | Times Cited: 1
End-to-End Differentiable Force Field Generator with Crystal Structure Differentiation and Matching
Hiroshi Nakano, Shinnosuke Hattori, H. Kobayashi, et al.
(2023)
Open Access
NICE-FF: A non-empirical, intermolecular, consistent, and extensible force field for nucleic acids and beyond
Gözde İniş Demir, Adem Tekin
The Journal of Chemical Physics (2023) Vol. 159, Iss. 24
Closed Access
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