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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:

MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows
Pavlo O. Dral, Fuchun Ge, Yi-Fan Hou, et al.
Journal of Chemical Theory and Computation (2024) Vol. 20, Iss. 3, pp. 1193-1213
Open Access | Times Cited: 30

Showing 1-25 of 30 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
ANI-1ccx-gelu Universal Interatomic Potential and Its Fine-Tuning: Toward Accurate and Efficient Anharmonic Vibrational Frequencies
Seyedeh Fatemeh Alavi, Yuxinxin Chen, Yi-Fan Hou, et al.
The Journal of Physical Chemistry Letters (2025), pp. 483-493
Closed Access | Times Cited: 2
MLatom Software Ecosystem for Surface Hopping Dynamics in Python with Quantum Mechanical and Machine Learning Methods
Lina Zhang, Sebastian V. Pios, Mikołaj Martyka, et al.
Journal of Chemical Theory and Computation (2024) Vol. 20, Iss. 12, pp. 5043-5057
Open Access | Times Cited: 11
LASP to the Future of Atomic Simulation: Intelligence and Automation
X. H. Xie, Zhengxin Yang, Dongxiao Chen, et al.
Precision Chemistry (2024) Vol. 2, Iss. 12, pp. 612-627
Open Access | Times Cited: 11
Machine-Learned Kohn–Sham Hamiltonian Mapping for Nonadiabatic Molecular Dynamics
Mohammad Shakiba, Alexey V. Akimov
Journal of Chemical Theory and Computation (2024) Vol. 20, Iss. 8, pp. 2992-3007
Closed Access | Times Cited: 10
ULaMDyn: Enhancing Excited-State Dynamics Analysis Through Streamlined Unsupervised Learning
Max Pinheiro, Matheus de Oliveira Bispo, Rafael S. Mattos, et al.
Digital Discovery (2025)
Open Access | Times Cited: 1
DeePMD-GNN: A DeePMD-kit Plugin for External Graph Neural Network Potentials
Jinzhe Zeng, Timothy J. Giese, Duo Zhang, et al.
Journal of Chemical Information and Modeling (2025)
Closed Access | Times Cited: 1
AI in computational chemistry through the lens of a decade-long journey
Pavlo O. Dral
Chemical Communications (2024) Vol. 60, Iss. 24, pp. 3240-3258
Open Access | Times Cited: 7
Physics-Informed Active Learning for Accelerating Quantum Chemical Simulations
Yi-Fan Hou, Lina Zhang, Quanhao Zhang, et al.
Journal of Chemical Theory and Computation (2024)
Open Access | Times Cited: 6
FeNNol: An efficient and flexible library for building force-field-enhanced neural network potentials
Thomas Plé, Olivier Adjoua, Louis Lagardère, et al.
The Journal of Chemical Physics (2024) Vol. 161, Iss. 4
Open Access | Times Cited: 5
Metal–Organic Frameworks through the Lens of Artificial Intelligence: A Comprehensive Review
Kevizali Neikha, Амрит Пузари
Langmuir (2024) Vol. 40, Iss. 42, pp. 21957-21975
Closed Access | Times Cited: 5
Universal and Updatable Artificial Intelligence-Enhanced Quantum Chemical Foundational Models
Yuxinxin Chen, Yi-Fan Hou, Olexandr Isayev, et al.
(2024)
Open Access | Times Cited: 4
Legion: A Platform for Gaussian Wavepacket Nonadiabatic Dynamics
Rafael S. Mattos, Saikat Mukherjee, Mario Barbatti
Journal of Chemical Theory and Computation (2025)
Closed Access
The Future of Catalysis: Applying Graph Neural Networks for Intelligent Catalyst Design
Zhihao Wang, Wentao Li, Siying Wang, et al.
Wiley Interdisciplinary Reviews Computational Molecular Science (2025) Vol. 15, Iss. 2
Closed Access
Alternating Donor–Acceptor Thienoacenes Featuring Up to 23 Linearly Fused Rings
Jiancheng Song, Xinyu Tong, Jingjing Guo, et al.
Organic Letters (2025)
Closed Access
Accurate and Affordable Simulation of Molecular Infrared Spectra with AIQM Models
Yi-Fan Hou, Cheng Wang, Pavlo O. Dral
The Journal of Physical Chemistry A (2025)
Closed Access
DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials
Jinzhe Zeng, Duo Zhang, Anyang Peng, et al.
Journal of Chemical Theory and Computation (2025)
Closed Access
Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning
Mikołaj Martyka, Lina Zhang, Fuchun Ge, et al.
npj Computational Materials (2025) Vol. 11, Iss. 1
Open Access
Interoperable workflows by exchanging grid-based data between quantum-chemical program packages
Kevin Focke, Matteo De Santis, Mario Wolter, et al.
The Journal of Chemical Physics (2024) Vol. 160, Iss. 16
Open Access | Times Cited: 3
Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning
Mikołaj Martyka, Lina Zhang, Fuchun Ge, et al.
(2024)
Open Access | Times Cited: 3
Quantum Dynamics from Classical Trajectories
Rafael S. Mattos, Saikat Mukherjee, Mario Barbatti
Journal of Chemical Theory and Computation (2024)
Closed Access | Times Cited: 3
: A Toolkit for Autonomous, User-Guided Construction of Machine-Learned Potential Energy Surfaces
Kai Töpfer, Luis Itza Vazquez-Salazar, Markus Meuwly
Computer Physics Communications (2024), pp. 109446-109446
Open Access | Times Cited: 3
Surprising new dynamics phenomena in Diels–Alder reaction of C60 uncovered with AI
Yi-Fan Hou, Quanhao Zhang, Pavlo O. Dral
(2024)
Open Access | Times Cited: 2
Molecular quantum chemical data sets and databases for machine learning potentials
Arif Ullah, Yuxinxin Chen, Pavlo O. Dral
Machine Learning Science and Technology (2024) Vol. 5, Iss. 4, pp. 041001-041001
Open Access | Times Cited: 2
Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training Exemplified for Glycine
Fuchun Ge, Ran Wang, Chen Qu, et al.
The Journal of Physical Chemistry Letters (2024) Vol. 15, Iss. 16, pp. 4451-4460
Open Access | Times Cited: 1
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