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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:

Informing geometric deep learning with electronic interactions to accelerate quantum chemistry
Zhuoran Qiao, Anders S. Christensen, Matthew Welborn, et al.
Proceedings of the National Academy of Sciences (2022) Vol. 119, Iss. 31
Open Access | Times Cited: 54

Showing 1-25 of 54 citing articles:

Scientific discovery in the age of artificial intelligence
Hanchen Wang, Tianfan Fu, Yuanqi Du, et al.
Nature (2023) Vol. 620, Iss. 7972, pp. 47-60
Closed Access | Times Cited: 588
CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling
Bowen Deng, Peichen Zhong, KyuJung Jun, et al.
Nature Machine Intelligence (2023) Vol. 5, Iss. 9, pp. 1031-1041
Open Access | Times Cited: 184
General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian
Xiaoxun Gong, He Li, Nianlong Zou, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 42
Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing
Yusong Wang, Tong Wang, Shaoning Li, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 17
Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations
Guanjie Wang, Changrui Wang, Xuanguang Zhang, et al.
iScience (2024) Vol. 27, Iss. 5, pp. 109673-109673
Open Access | Times Cited: 12
Fast uncertainty estimates in deep learning interatomic potentials
Albert Zhu, Simon Batzner, Albert Musaelian, et al.
The Journal of Chemical Physics (2023) Vol. 158, Iss. 16
Open Access | Times Cited: 30
Computational Studies of Aflatoxin B1 (AFB1): A Review
Joel Martínez, Maricarmen Hernández‐Rodríguez, Abraham Méndez‐Albores, et al.
Toxins (2023) Vol. 15, Iss. 2, pp. 135-135
Open Access | Times Cited: 29
Exploring protein–ligand binding affinity prediction with electron density-based geometric deep learning
Clemens Isert, Kenneth Atz, Sereina Riniker, et al.
RSC Advances (2024) Vol. 14, Iss. 7, pp. 4492-4502
Open Access | Times Cited: 9
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: 7
A review of displacement cascade simulations using molecular dynamics emphasizing interatomic potentials for TPBAR components
Ankit Roy, Giridhar Nandipati, Andrew M. Casella, et al.
npj Materials Degradation (2025) Vol. 9, Iss. 1
Open Access
An Importance Sampling Method for Generating Optimal Interpolation Points in Training Physics-Informed Neural Networks
Hui Li, Yichi Zhang, Zhaoxiong Wu, et al.
Mathematics (2025) Vol. 13, Iss. 1, pp. 150-150
Open Access
Efficient Sampling for Machine Learning Electron Density and Its Response in Real Space
Chaoqiang Feng, Yaolong Zhang, Bin Jiang
Journal of Chemical Theory and Computation (2025)
Open Access
Accurate Neural Network Fine-Tuning Approach for Transferable Ab Initio Energy Prediction across Varying Molecular and Crystalline Scales
Wai-Pan Ng, Zili Zhang, Jun Yang
Journal of Chemical Theory and Computation (2025)
Open Access
Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023
Igor Poltavsky, Anton Charkin-Gorbulin, Mirela Puleva, et al.
Chemical Science (2025)
Open Access
Thermodynamics and dielectric response of BaTiO3 by data-driven modeling
Lorenzo Gigli, Max Veit, Michele Kotiuga, et al.
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 28
Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
Peter Bjørn Jørgensen, Arghya Bhowmik
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 26
Taking advantage of noise in quantum reservoir computing
L. Domingo, Gabriel G. Carlo, F. Borondo
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 17
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: 16
Acceleration of Graph Neural Network-Based Prediction Models in Chemistry via Co-Design Optimization on Intelligence Processing Units
Hatem Helal, Jesun Firoz, Jenna A. Bilbrey, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 5, pp. 1568-1580
Closed Access | Times Cited: 4
Exploration of the Two-Electron Excitation Space with Data-Driven Coupled Cluster
P.D.Varuna S. Pathirage, Justin T. Phillips, Konstantinos D. Vogiatzis
The Journal of Physical Chemistry A (2024) Vol. 128, Iss. 10, pp. 1938-1947
Closed Access | Times Cited: 4
Force-field-enhanced neural network interactions: from local equivariant embedding to atom-in-molecule properties and long-range effects
Thomas Plé, Louis Lagardère, Jean‐Philip Piquemal
Chemical Science (2023) Vol. 14, Iss. 44, pp. 12554-12569
Open Access | Times Cited: 11
Applications of machine‐learning interatomic potentials for modeling ceramics, glass, and electrolytes: A review
Shingo Urata, Marco Bertani, Alfonso Pedone
Journal of the American Ceramic Society (2024)
Closed Access | Times Cited: 4
Higher-order equivariant neural networks for charge density prediction in materials
Thomas Edward Koker, Keegan Quigley, Eric Taw, et al.
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 3
Putting Chemical Knowledge to Work in Machine Learning for Reactivity
Kjell Jorner
CHIMIA International Journal for Chemistry (2023) Vol. 77, Iss. 1/2, pp. 22-22
Open Access | Times Cited: 10
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