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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:

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

Showing 21 citing articles:

Revealing the role of double-layer microenvironments in pH-dependent oxygen reduction activity over metal-nitrogen-carbon catalysts
Peng Li, Yuzhou Jiao, Yaner Ruan, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 88
Electric Double Layer Effects in Electrocatalysis: Insights from Ab Initio Simulation and Hierarchical Continuum Modeling
Peng Li, Yuzhou Jiao, Jun Huang, et al.
JACS Au (2023) Vol. 3, Iss. 10, pp. 2640-2659
Open Access | Times Cited: 56
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
Understanding and Tuning the Effects of H2O on Catalytic CO and CO2 Hydrogenation
Mingrui Wang, Guanghui Zhang, Hao Wang, et al.
Chemical Reviews (2024) Vol. 124, Iss. 21, pp. 12006-12085
Closed Access | Times Cited: 11
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
Probing additives for green lubricants with the aid of machine learning molecular dynamics: The case of gallate molecules for aqueous solutions
Huong Ta Thi Thuy, Mauro Ferrario, Sophie Loehlé, et al.
Applied Surface Science (2025), pp. 162836-162836
Closed Access | Times Cited: 1
Probing the electric double layer structure at nitrogen-doped graphite electrodes by constant-potential molecular dynamics simulations
Legeng Yu, Nan Yao, Yuchen Gao, et al.
Journal of Energy Chemistry (2024) Vol. 93, pp. 299-305
Closed Access | Times Cited: 7
Directly predicting N2 electroreduction reaction free energy using interpretable machine learning with non-DFT calculated features
Yaqin Zhang, Yuhang Wang, Ninggui Ma, et al.
Journal of Energy Chemistry (2024) Vol. 97, pp. 139-148
Closed Access | Times Cited: 7
Modelling electrified microporous carbon/electrolyte electrochemical interface and unraveling charge storage mechanism by machine learning accelerated molecular dynamics
Yifeng Zhang, Hui Huang, Jie Tian, et al.
Energy storage materials (2023) Vol. 63, pp. 103069-103069
Closed Access | Times Cited: 11
Ab initio informed machine learning potential for tribochemistry and mechanochemistry: Application for eco–friendly gallate lubricant additive
Huong T. T. Ta, Mauro Ferrario, Sophie Loehlé, et al.
Deleted Journal (2024) Vol. 1, pp. 100005-100005
Open Access | Times Cited: 4
Water-Mediated Proton Hopping Mechanisms at the SnO2(110)/H2O Interface from Ab Initio Deep Potential Molecular Dynamics
Mei Jia, Yong‐Bin Zhuang, Feng Wang, et al.
Precision Chemistry (2024) Vol. 2, Iss. 12, pp. 644-654
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
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
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
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
Auxiliary guidance manufacture and revealing potential mechanism of perovskite solar cell using machine learning
Quan Zhang, Jianqi Wang, Guohua Liu
Journal of Energy Chemistry (2023) Vol. 86, pp. 146-157
Closed Access | Times Cited: 7
Electrochemical CO2 Reduction to Multicarbon Products on Non‐Copper Based Catalysts
Jiayi Huang, Qianwen Liu, Jianmei Huang, et al.
ChemSusChem (2024)
Closed Access | Times Cited: 2
High-Throughput Aqueous Electrolyte Structure Prediction Using IonSolvR and Equivariant Graph Neural Network Potentials
Sophie Baker, Joshua Pagotto, Timothy T. Duignan, et al.
The Journal of Physical Chemistry Letters (2023) Vol. 14, Iss. 42, pp. 9508-9515
Closed Access | Times Cited: 6
Universal machine learning potential accelerates atomistic modeling of materials
Zhongheng Fu, Dawei Zhang
Journal of Energy Chemistry (2023) Vol. 83, pp. 1-2
Closed Access | Times Cited: 2
Synergistic role of Cl− and Br− ions in growth control and mechanistic insights of high aspect ratio silver nanowires for flexible transparent conductive films
Jia-Lei Xu, Ruidong Shi, Haiping Zhou, et al.
Nanoscale (2024)
Closed Access
Research on the wellbore cleaning mechanism and prediction of cleaning ability of well-flushing fluid based on experiment-molecular dynamics simulation-machine learning
Hanxuan Song, Fuli Li, Binru Li, et al.
Separation and Purification Technology (2024), pp. 130875-130875
Closed Access
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