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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 Interatomic Potentials for Reactive Hydrogen Dynamics at Metal Surfaces Based on Iterative Refinement of Reaction Probabilities
Wojciech G. Stark, Julia Westermayr, Oscar A. Douglas‐Gallardo, et al.
The Journal of Physical Chemistry C (2023) Vol. 127, Iss. 50, pp. 24168-24182
Open Access | Times Cited: 21

Showing 21 citing articles:

A Euclidean transformer for fast and stable machine learned force fields
J. Thorben Frank, Oliver T. Unke, Klaus‐Robert Müller, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 18
An overview about neural networks potentials in molecular dynamics simulation
Raidel Martin‐Barrios, Edisel Navas‐Conyedo, Xuyi Zhang, et al.
International Journal of Quantum Chemistry (2024) Vol. 124, Iss. 11
Closed Access | Times Cited: 11
Applications of machine learning in surfaces and interfaces
Shaofeng Xu, Jing‐Yuan Wu, Ying Guo, et al.
Chemical Physics Reviews (2025) Vol. 6, Iss. 1
Open Access | Times Cited: 1
Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces
Wojciech G. Stark, Cas van der Oord, Ilyes Batatia, et al.
Machine Learning Science and Technology (2024) Vol. 5, Iss. 3, pp. 030501-030501
Open Access | Times Cited: 8
SpaiNN: equivariant message passing for excited-state nonadiabatic molecular dynamics
Sascha Mausenberger, Carolin Müller, Alexandre Tkatchenko, et al.
Chemical Science (2024) Vol. 15, Iss. 38, pp. 15880-15890
Open Access | Times Cited: 4
Machine Learning Potentials for Heterogeneous Catalysis
Amir Omranpour, Jan Elsner, K. Nikolas Lausch, et al.
ACS Catalysis (2025) Vol. 15, Iss. 3, pp. 1616-1634
Open Access
Surface-subsurface transport of hydrogen and hot H promoted H2 recombination rate constant on Ni(110)
Wenji Wang
International Journal of Hydrogen Energy (2025) Vol. 113, pp. 241-248
Closed Access
Application of Machine Learning Interatomic Potentials in Heterogeneous Catalysis
Gbolagade Olajide, Khagendra Baral, Sophia Ezendu, et al.
(2025)
Closed Access
Impact of crystal structure symmetry in training datasets on GNN-based energy assessments for chemically disordered CsPbI3
A. Krautsou, Innokentiy S. Humonen, Vladimir D. Lazarev, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Application of machine learning interatomic potentials in heterogeneous catalysis
Gbolagade Olajide, Khagendra Baral, Sophia Ezendu, et al.
Journal of Catalysis (2025), pp. 116202-116202
Closed Access
TrIP2: Expanding the Transformer Interatomic Potential Demonstrates Architectural Scalability for Organic Compounds
Joshua L. Ebbert, Bryce Hedelius, Jyothish Joy, et al.
The Journal of Physical Chemistry A (2025)
Closed Access
Thermal Rates and High-Temperature Tunneling from Surface Reaction Dynamics and First-Principles
Florian Nitz, Liang Zhang, Nils Hertl, et al.
Journal of the American Chemical Society (2024) Vol. 146, Iss. 46, pp. 31538-31546
Open Access | Times Cited: 3
Spiers Memorial Lecture: New directions in molecular scattering
George C. Schatz, Alec M. Wodtke, Xueming Yang
Faraday Discussions (2024) Vol. 251, pp. 9-62
Open Access | Times Cited: 1
Virtual Special Issue on Machine Learning in Physical Chemistry Volume 2
Andrew L. Ferguson, Jim Pfaendtner
The Journal of Physical Chemistry B (2024) Vol. 128, Iss. 27, pp. 6435-6438
Closed Access | Times Cited: 1
Challenges and Opportunities for Machine Learning Potentials in Transition Path Sampling: Alanine Dipeptide and Azobenzene Studies
Nikita Fedik, Wei Li, Nicholas Lubbers, et al.
(2024)
Open Access | Times Cited: 1
Structure and transport properties of LiTFSI-based deep eutectic electrolytes from machine learning interatomic potential simulations
Omid Shayestehpour, Stefan Zahn
(2024)
Open Access | Times Cited: 1
Structure and transport properties of LiTFSI-based deep eutectic electrolytes from machine-learned interatomic potential simulations
Omid Shayestehpour, Stefan Zahn
The Journal of Chemical Physics (2024) Vol. 161, Iss. 13
Closed Access | Times Cited: 1
SpaiNN: Equivariant Message Passing for Excited-State Nonadiabatic Molecular Dynamics
Sascha Mausenberger, Carolin Müller, Alexandre Tkatchenko, et al.
(2024)
Open Access
Virtual Special Issue on Machine Learning in Physical Chemistry Volume 2
Andrew L. Ferguson, Jim Pfaendtner
The Journal of Physical Chemistry A (2024) Vol. 128, Iss. 27, pp. 5225-5228
Closed Access
Virtual Special Issue on Machine Learning in Physical Chemistry Volume 2
Andrew L. Ferguson, Jim Pfaendtner
The Journal of Physical Chemistry C (2024) Vol. 128, Iss. 27, pp. 11079-11082
Closed Access
Predictive evaluation of hydrogen diffusion coefficient on Pd(111) surface by path integral simulations using neural network potential
Yuta Kataoka, Jun Haruyama, Osamu Sugino, et al.
Physical Review Research (2024) Vol. 6, Iss. 4
Open Access
Empirical Determination of Scattering Matrices from Magnetic Molecular Interferometry for Gas–Surface Collisions
Helen Chadwick, Gil Alexandrowicz
The Journal of Physical Chemistry C (2024) Vol. 128, Iss. 49, pp. 20913-20922
Open Access
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