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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:

Using neurocomputing techniques to determine microstructural properties in a Li-ion battery
Simran Sandhu, Ramavtar Tyagi, Elahe Talaie, et al.
Neural Computing and Applications (2022) Vol. 34, Iss. 12, pp. 9983-9999
Closed Access | Times Cited: 10

Showing 10 citing articles:

Metal Oxide Wrapped by Reduced Graphene Oxide Nanocomposites as Anode Materials for Lithium-Ion Batteries
Junaid Aslam, Yong Wang
Nanomaterials (2023) Vol. 13, Iss. 2, pp. 296-296
Open Access | Times Cited: 20
Research Progress on Data-Driven Methods for Battery States Estimation of Electric Buses
Dengfeng Zhao, Haiyang Li, Fang Zhou, et al.
World Electric Vehicle Journal (2023) Vol. 14, Iss. 6, pp. 145-145
Open Access | Times Cited: 12
Computational Framework Combining Quantum Mechanics, Molecular Dynamics, and Deep Neural Networks to Evaluate the Intrinsic Properties of Materials
Amirmasoud Lanjan, Zahra Moradi, Seshasai Srinivasan
The Journal of Physical Chemistry A (2023) Vol. 127, Iss. 31, pp. 6603-6613
Closed Access | Times Cited: 5
A computational framework for evaluating molecular dynamics potential parameters employing quantum mechanics
Amirmasoud Lanjan, Zahra Moradi, Seshasai Srinivasan
Molecular Systems Design & Engineering (2023) Vol. 8, Iss. 5, pp. 632-646
Closed Access | Times Cited: 4
Swift Prediction of Battery Performance: Applying Machine Learning Models on Microstructural Electrode Images for Lithium-Ion Batteries
Patrick Deeg, Christian Weisenberger, Jonas Oehm, et al.
Batteries (2024) Vol. 10, Iss. 3, pp. 99-99
Open Access | Times Cited: 1
AI-Based Nano-Scale Material Property Prediction for Li-Ion Batteries
M. Lal, Akashdeep Singh, Ryan Mzik, et al.
Batteries (2024) Vol. 10, Iss. 2, pp. 51-51
Open Access | Times Cited: 1
Advancements in cathode materials for lithium-ion batteries: an overview of future prospects
N. Gopalakrishnan, Nanthini Mohana Suntharam, Shahid Bashir, et al.
Ionics (2024)
Closed Access | Times Cited: 1
Co-doping studies to enhance the life and electro-chemo-mechanical properties of the LiMn2O4cathode using multi-scale modeling and neuro-computing techniques
Ramavtar Tyagi, Seshasai Srinivasan
Physical Chemistry Chemical Physics (2022) Vol. 24, Iss. 31, pp. 18645-18666
Closed Access | Times Cited: 3
Software Suite for Self-Paced Learning
Meng Zhang, Zi Xu Gu, Adan Amer, et al.
International Journal of Emerging Technologies in Learning (iJET) (2022) Vol. 17, Iss. 19, pp. 20-32
Open Access | Times Cited: 1
AI-based Nano-scale Material Property Prediction For Li-Ion Batteries
M. Lal, Akashdeep Singh, Ryan Mzik, et al.
(2023)
Open Access
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