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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:

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: 11

Showing 11 citing articles:

Computational Methods for Modeling Lipid-Mediated Active Pharmaceutical Ingredient Delivery
Markéta Paloncýová, Mariana Valério, Ricardo Nascimento dos Santos, et al.
Molecular Pharmaceutics (2025)
Open Access | Times Cited: 2
Machine Learning-Based Molecular Dynamics Studies on Predicting Thermophysical Properties of Ethanol–Octane Blends
Amirali Shateri, Zhiyin Yang, Jianfei Xie
Energy & Fuels (2025)
Closed Access | Times Cited: 1
From Ab Initio to Instrumentation: A Field Guide to Characterizing Multivalent Liquid Electrolytes
Glenn Pastel, Travis P. Pollard, Oleg Borodin, et al.
Chemical Reviews (2025)
Closed Access | Times Cited: 1
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 | Times Cited: 1
Unsupervised Machine Learning in the Analysis of Nonadiabatic Molecular Dynamics Simulation
Yifei Zhu, Jiawei Peng, Chao Xu, et al.
The Journal of Physical Chemistry Letters (2024), pp. 9601-9619
Closed Access | Times Cited: 4
Improved Description of Environment and Vibronic Effects with Electrostatically Embedded ML Potentials
Kirill Zinovjev, Carles Curutchet
The Journal of Physical Chemistry Letters (2025), pp. 774-781
Closed 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
The evolution of machine learning potentials for molecules, reactions and materials
Junfan Xia, Yaolong Zhang, Bin Jiang
Chemical Society Reviews (2025)
Open Access
QM9star, two Million DFT-computed Equilibrium Structures for Ions and Radicals with Atomic Information
Miao‐Jiong Tang, Tiancheng Zhu, Shuo‐Qing Zhang, et al.
Scientific Data (2024) Vol. 11, Iss. 1
Open Access | Times Cited: 2
A Globally Accurate Neural Network Potential Energy Surface and Quantum Dynamics Studies on Be+(2S) + H2/D2 → BeH+/BeD+ + H/D Reactions
Zijiang Yang, Furong Cao, Huiying Cheng, et al.
Molecules (2024) Vol. 29, Iss. 14, pp. 3436-3436
Open Access | Times Cited: 1
Toward Long‐Life High‐Voltage Aqueous Li‐Ion Batteries: from Solvation Chemistry to Solid‐Electrolyte‐Interphase Layer Optimization Against Electron Tunneling Effect
Insu Jeong, Sungho Kim, Youngbi Kim, et al.
Advanced Materials (2024)
Closed Access | Times Cited: 1
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