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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:

Development and Validation of Versatile Deep Atomistic Potentials for Metal Oxides
Pandu Wisesa, Christopher M. Andolina, Wissam A. Saidi
The Journal of Physical Chemistry Letters (2023) Vol. 14, Iss. 2, pp. 468-475
Closed Access | Times Cited: 15

Showing 15 citing articles:

Accelerating Li-based battery design by computationally engineering materials
Sandip Maiti, Matthew T. Curnan, Kakali Maiti, et al.
Chem (2023) Vol. 9, Iss. 12, pp. 3415-3460
Closed Access | Times Cited: 13
Deep-learning neural network potentials for titanate perovskites
Pandu Wisesa, Terumasa Tadano, Wissam A. Saidi
Computational Materials Science (2025) Vol. 250, pp. 113719-113719
Closed Access
Pairwise force field for magnesium oxide atomistic simulations
Luis Gustavo Vieira Gonçalves, José Pedro Rino
Physica B Condensed Matter (2025) Vol. 702, pp. 416983-416983
Closed Access
First-principles study of the properties of plutonium oxides and their interfaces based on machine learning
Yilin Fang, Jintao Wang, Zexin Jiang, et al.
Materials Today Communications (2025), pp. 112372-112372
Closed Access
Machine-Learning Accelerated First-Principles Accurate Modeling of the Solid–Liquid Phase Transition in MgO under Mantle Conditions
Pandu Wisesa, Christopher M. Andolina, Wissam A. Saidi
The Journal of Physical Chemistry Letters (2023) Vol. 14, Iss. 39, pp. 8741-8748
Closed Access | Times Cited: 10
ColabFit exchange: Open-access datasets for data-driven interatomic potentials
Joshua A. Vita, Eric G. Fuemmeler, Amit Gupta, et al.
The Journal of Chemical Physics (2023) Vol. 159, Iss. 15
Open Access | Times Cited: 9
Active Sparse Bayesian Committee Machine Potential for Isothermal-Isobaric Molecular Dynamics Simulations
Soohaeng Yoo Willow, Dong Geon Kim, R. Sundheep, et al.
Physical Chemistry Chemical Physics (2024) Vol. 26, Iss. 33, pp. 22073-22082
Open Access | Times Cited: 3
Exploring the formation of gold/silver nanoalloys with gas-phase synthesis and machine-learning assisted simulations
Quentin Gromoff, Patrizio Benzo, Wissam A. Saidi, et al.
Nanoscale (2023) Vol. 16, Iss. 1, pp. 384-393
Open Access | Times Cited: 9
Highly transferable atomistic machine-learning potentials from curated and compact datasets across the periodic table
Christopher M. Andolina, Wissam A. Saidi
Digital Discovery (2023) Vol. 2, Iss. 4, pp. 1070-1077
Open Access | Times Cited: 7
Theoretical Prediction of Thermal Expansion Anisotropy for Y2Si2O7 Environmental Barrier Coatings Using a Deep Neural Network Potential and Comparison to Experiment
Cameron J. Bodenschatz, Wissam A. Saidi, Jamesa L. Stokes, et al.
Materials (2024) Vol. 17, Iss. 2, pp. 286-286
Open Access | Times Cited: 2
ChecMatE: A workflow package to automatically generate machine learning potentials and phase diagrams for semiconductor alloys
Yuxin Guo, Yong‐Bin Zhuang, Jueli Shi, et al.
The Journal of Chemical Physics (2023) Vol. 159, Iss. 9
Closed Access | Times Cited: 6
Predicting melting temperatures across the periodic table with machine learning atomistic potentials
Christopher M. Andolina, Wissam A. Saidi
Digital Discovery (2024) Vol. 3, Iss. 7, pp. 1421-1429
Open Access | Times Cited: 1
Overcoming Inaccuracies in Machine Learning Interatomic Potential Implementation for Ionic Vacancy Simulations
Pandu Wisesa, Wissam A. Saidi
The Journal of Physical Chemistry Letters (2024), pp. 31-37
Closed Access | Times Cited: 1
Exploring the Crystal Structure and Electronic Properties of γ-Al2O3: Machine Learning Drives Future Material Innovations
Zhenyu Bu, Yun Xue, Xiaoqin Zhao, et al.
ACS Applied Materials & Interfaces (2024) Vol. 16, Iss. 44, pp. 60458-60471
Closed Access
Determination of the vacancy distribution over Al cation sites in γ-Al2O3
Henry O. Ayoola, Cheng-Han Li, Stephen D. House, et al.
Materials Advances (2023) Vol. 5, Iss. 3, pp. 1078-1087
Open Access | Times Cited: 1
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