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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:

Flow-Matching: Efficient Coarse-Graining of Molecular Dynamics without Forces
Jonas Köhler, Yaoyi Chen, Andreas Krämer, et al.
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 3, pp. 942-952
Open Access | Times Cited: 32

Showing 1-25 of 32 citing articles:

Perspective: Advances, Challenges, and Insight for Predictive Coarse-Grained Models
W. G. Noid
The Journal of Physical Chemistry B (2023) Vol. 127, Iss. 19, pp. 4174-4207
Open Access | Times Cited: 78
Machine learned coarse-grained protein force-fields: Are we there yet?
Aleksander E. P. Durumeric, Nicholas E. Charron, Clark Templeton, et al.
Current Opinion in Structural Biology (2023) Vol. 79, pp. 102533-102533
Open Access | Times Cited: 53
Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics
Marloes Arts, Víctor García Satorras, Chin‐Wei Huang, et al.
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 18, pp. 6151-6159
Open Access | Times Cited: 45
The Physics-AI Dialogue in Drug Design
Pablo Andrés Vargas-Rosales, Amedeo Caflisch
RSC Medicinal Chemistry (2025)
Open Access | Times Cited: 1
Scaling Graph Neural Networks to Large Proteins
Justin Airas, Bin Zhang
Journal of Chemical Theory and Computation (2025)
Open Access | Times Cited: 1
Ensuring thermodynamic consistency with invertible coarse-graining
Shriram Chennakesavalu, David J. Toomer, Grant M. Rotskoff
The Journal of Chemical Physics (2023) Vol. 158, Iss. 12
Open Access | Times Cited: 22
Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics
Andreas Krämer, Aleksander E. P. Durumeric, Nicholas E. Charron, et al.
The Journal of Physical Chemistry Letters (2023) Vol. 14, Iss. 17, pp. 3970-3979
Open Access | Times Cited: 20
Structural ensembles of disordered proteins from hierarchical chain growth and simulation
Lisa M. Pietrek, Lukas S. Stelzl, Gerhard Hummer
Current Opinion in Structural Biology (2022) Vol. 78, pp. 102501-102501
Open Access | Times Cited: 23
Transferable Implicit Solvation via Contrastive Learning of Graph Neural Networks
Justin Airas, Xinqiang Ding, Bin Zhang
ACS Central Science (2023) Vol. 9, Iss. 12, pp. 2286-2297
Open Access | Times Cited: 14
Coarse-Grained Many-Body Potentials of Ligand-Stabilized Nanoparticles from Machine-Learned Mean Forces
G. Giunta, Gerardo Campos-Villalobos, Marjolein Dijkstra
ACS Nano (2023) Vol. 17, Iss. 23, pp. 23391-23404
Open Access | Times Cited: 12
DiAMoNDBack: Diffusion-Denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein Traces
Michael S. Jones, Kirill Shmilovich, Andrew L. Ferguson
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 21, pp. 7908-7923
Closed Access | Times Cited: 11
Machine learning in biological physics: From biomolecular prediction to design
Jonathan Martin, M. Mateos, José N. Onuchic, et al.
Proceedings of the National Academy of Sciences (2024) Vol. 121, Iss. 27
Open Access | Times Cited: 4
On the emergence of machine-learning methods in bottom-up coarse-graining
Patrick G. Sahrmann, Gregory A. Voth
Current Opinion in Structural Biology (2025) Vol. 90, pp. 102972-102972
Closed Access
chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics
Paul Fuchs, Stephan Thaler, Sebastien Röcken, et al.
Computer Physics Communications (2025), pp. 109512-109512
Open Access
Large language model-driven probabilistic trajectory prediction in the Internet of Things using spatio-temporal encoding and normalizing flows
Xiaoliang Wang, Liming Xu, Lian Zhou, et al.
Digital Communications and Networks (2025)
Open Access
Unveiling Interactions of a Peptide-Bound Monolayer-Protected Metal Nanocluster with a Lipid Bilayer
Soumya Mondal, Tarak Karmakar
The Journal of Physical Chemistry Letters (2025), pp. 3351-3358
Closed Access
Toward Predictive Coarse-Grained Simulations of Biomolecular Condensates
Shuming Liu, Cong Wang, Bin Zhang
Biochemistry (2025)
Closed Access
A Hybrid Bottom-Up and Data-Driven Machine Learning Approach for Accurate Coarse-Graining of Large Molecular Complexes
Korbinian Liebl, Gregory A. Voth
Journal of Chemical Theory and Computation (2025)
Closed Access
Top-Down Machine Learning of Coarse-Grained Protein Force Fields
Carles Navarro, Maciej Majewski, Gianni De Fabritiis
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 21, pp. 7518-7526
Open Access | Times Cited: 10
Conditioning Boltzmann generators for rare event sampling
Sebastian Falkner, Alessandro Coretti, Salvatore Romano, et al.
Machine Learning Science and Technology (2023) Vol. 4, Iss. 3, pp. 035050-035050
Open Access | Times Cited: 9
Sifting through the Noise: A Survey of Diffusion Probabilistic Models and Their Applications to Biomolecules
Trevor A. Norton, Debswapna Bhattacharya
Journal of Molecular Biology (2024), pp. 168818-168818
Open Access | Times Cited: 2
Molecular Dynamics Simulations for Rationalizing Polymer Bioconjugation Strategies: Challenges, Recent Developments, and Future Opportunities
Josef Kehrein, Christoph Sotriffer
ACS Biomaterials Science & Engineering (2023) Vol. 10, Iss. 1, pp. 51-74
Closed Access | Times Cited: 6
Multibody Terms in Protein Coarse-Grained Models: A Top-Down Perspective
Iryna Zaporozhets, Cecilia Clementi
The Journal of Physical Chemistry B (2023) Vol. 127, Iss. 31, pp. 6920-6927
Closed Access | Times Cited: 5
Neural potentials of proteins extrapolate beyond training data
Geemi P. Wellawatte, Glen M. Hocky, Andrew Dickson White
The Journal of Chemical Physics (2023) Vol. 159, Iss. 8
Open Access | Times Cited: 4
Enhanced Coarse-Grained Molecular Dynamics Simulation with a Smoothed Hybrid Potential Using a Neural Network Model
Ryo Kanada, Atsushi Tokuhisa, Yusuke Nagasaka, et al.
Journal of Chemical Theory and Computation (2023) Vol. 20, Iss. 1, pp. 7-17
Closed Access | Times Cited: 4
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