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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:

Insights into lithium manganese oxide–water interfaces using machine learning potentials
Marco Eckhoff, Jörg Behler
The Journal of Chemical Physics (2021) Vol. 155, Iss. 24
Open Access | Times Cited: 39

Showing 1-25 of 39 citing articles:

Oxide– and Silicate–Water Interfaces and Their Roles in Technology and the Environment
Jose Bañuelos, Eric Borguet, Gordon E. Brown, et al.
Chemical Reviews (2023) Vol. 123, Iss. 10, pp. 6413-6544
Open Access | Times Cited: 97
Improving the Accuracy of Atomistic Simulations of the Electrochemical Interface
Ravishankar Sundararaman, Derek Vigil‐Fowler, Kathleen Schwarz
Chemical Reviews (2022) Vol. 122, Iss. 12, pp. 10651-10674
Open Access | Times Cited: 84
How to train a neural network potential
Alea Miako Tokita, Jörg Behler
The Journal of Chemical Physics (2023) Vol. 159, Iss. 12
Open Access | Times Cited: 50
2023 roadmap for potassium-ion batteries
Yang Xu, Maria‐Magdalena Titirici, Jingwei Chen, et al.
Journal of Physics Energy (2023) Vol. 5, Iss. 2, pp. 021502-021502
Open Access | Times Cited: 44
Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials
Amir Omranpour, Pablo Montero de Hijes, Jörg Behler, et al.
The Journal of Chemical Physics (2024) Vol. 160, Iss. 17
Open Access | Times Cited: 23
Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials—A Review
Kaiwei Wan, Jianxin He, Xinghua Shi
Advanced Materials (2023) Vol. 36, Iss. 22
Closed Access | Times Cited: 29
Machine Learning Assisted Simulations of Electrochemical Interfaces: Recent Progress and Challenges
Yipeng Zhou, Yixin Ouyang, Yehui Zhang, et al.
The Journal of Physical Chemistry Letters (2023) Vol. 14, Iss. 9, pp. 2308-2316
Closed Access | Times Cited: 26
Lifelong Machine Learning Potentials
Marco Eckhoff, Markus Reiher
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 12, pp. 3509-3525
Open Access | Times Cited: 23
Mechanism of Fe(II) Chemisorption on Hematite(001) Revealed by Reactive Neural Network Potential Molecular Dynamics
Kit Joll, Philipp Schienbein, Kevin M. Rosso, et al.
The Journal of Physical Chemistry Letters (2025), pp. 848-856
Open Access | Times Cited: 1
Defect Segregation, Water Layering, and Proton Transfer at Zirconium Oxynitride/Water Interface Examined Using Neural Network Potential
Akitaka Nakanishi, Shusuke Kasamatsu, Jun Haruyama, et al.
The Journal of Physical Chemistry C (2025)
Closed Access | Times Cited: 1
Measurements of the Electrostatic Potential at the Mineral/Electrolyte Interface
Tin Klačić, Jozefina Katić, Davor Kovačević, et al.
Reviews in Mineralogy and Geochemistry (2025) Vol. 91A, Iss. 1, pp. 295-336
Closed Access | Times Cited: 1
Molecular insight into the GaP(110)-water interface using machine learning accelerated molecular dynamics
Xue-Ting Fan, Xiaojian Wen, Yong‐Bin Zhuang, et al.
Journal of Energy Chemistry (2023) Vol. 82, pp. 239-247
Closed Access | Times Cited: 21
Atomistic neural network representations for chemical dynamics simulations of molecular, condensed phase, and interfacial systems: Efficiency, representability, and generalization
Yaolong Zhang, Qidong Lin, Bin Jiang
Wiley Interdisciplinary Reviews Computational Molecular Science (2022) Vol. 13, Iss. 3
Closed Access | Times Cited: 26
Intramolecular and Water Mediated Tautomerism of Solvated Glycine
Pengchao Zhang, Axel Tosello Gardini, Xuefei Xu, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 9, pp. 3599-3604
Open Access | Times Cited: 5
Improving Molecular‐Dynamics Simulations for Solid–Liquid Interfaces with Machine‐Learning Interatomic Potentials
Pengfei Hou, Yumiao Tian, Xing Meng
Chemistry - A European Journal (2024) Vol. 30, Iss. 49
Closed Access | Times Cited: 5
Nanosecond solvation dynamics of the hematite/liquid water interface at hybrid DFT accuracy using committee neural network potentials
Philipp Schienbein, Jochen Blumberger
Physical Chemistry Chemical Physics (2022) Vol. 24, Iss. 25, pp. 15365-15375
Open Access | Times Cited: 18
Constant-potential molecular dynamics simulation and its application in rechargeable batteries
Legeng Yu, Xiang Chen, Nan Yao, et al.
Journal of Materials Chemistry A (2023) Vol. 11, Iss. 21, pp. 11078-11088
Closed Access | Times Cited: 11
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
Machine Learning‐Assisted Simulations and Predictions for Battery Interfaces
Zhaojun Sun, Xin Li, Yiming Wu, et al.
Advanced Intelligent Systems (2025)
Open Access
Atomistic simulation of batteries via machine learning force fields: from bulk to interface
Jinkai Zhang, Yaopeng Li, Ming Chen, et al.
Journal of Energy Chemistry (2025)
Closed Access
Solvent effect on the adsorption of lithium polysulfides on single-atom Ni on N-doped graphene: a first-principles study
Jessie Manopo, Muhammad Khaishar Mahardhika, Charlie Ofiyen, et al.
Journal of Physics Conference Series (2025) Vol. 2980, Iss. 1, pp. 012036-012036
Open Access
Free energy profiles for chemical reactions in solution from high-dimensional neural network potentials: The case of the Strecker synthesis
Alea Miako Tokita, Timothée Devergne, A. Marco Saitta, et al.
The Journal of Chemical Physics (2025) Vol. 162, Iss. 17
Closed Access
Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance
Vidushi Sharma, Maxwell J. Giammona, Dmitry Yu. Zubarev, et al.
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 22, pp. 6998-7010
Open Access | Times Cited: 9
Applications and training sets of machine learning potentials
Chang‐Ho Hong, Jaehoon Kim, Jaesun Kim, et al.
Science and Technology of Advanced Materials Methods (2023) Vol. 3, Iss. 1
Open Access | Times Cited: 8
Oxidation-State Dynamics and Emerging Patterns in Magnetite
Emre Gürsoy, Gregor B. Vonbun-Feldbauer, Robert H. Meißner
The Journal of Physical Chemistry Letters (2023) Vol. 14, Iss. 30, pp. 6800-6807
Open Access | Times Cited: 7
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