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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:

Maximizing the energy density and stability of Ni-rich layered cathode materials with multivalent dopants via machine learning
Minseon Kim, Seungpyo Kang, Hyun Gyu Park, et al.
Chemical Engineering Journal (2022) Vol. 452, pp. 139254-139254
Closed Access | Times Cited: 29

Showing 1-25 of 29 citing articles:

Design of high‐performance and sustainable Co‐free Ni‐rich cathodes for next‐generation lithium‐ion batteries
Hao Ge, Zhiwen Shen, Yanhong Wang, et al.
SusMat (2023) Vol. 4, Iss. 1, pp. 48-71
Open Access | Times Cited: 40
Machine learning enabled customization of performance-oriented hydrogen storage materials for fuel cell systems
Panpan Zhou, Xuezhang Xiao, Xinyu Zhu, et al.
Energy storage materials (2023) Vol. 63, pp. 102964-102964
Closed Access | Times Cited: 33
Nickel-rich nickel–cobalt–manganese and nickel–cobalt–aluminum cathodes in lithium-ion batteries: Pathways for performance optimization
Abu Danish Aiman Bin Abu Sofian, Ibnu Syafiq Imaduddin, S.R. Majid, et al.
Journal of Cleaner Production (2023) Vol. 435, pp. 140324-140324
Closed Access | Times Cited: 33
Advances of high-performance LiNi1-x-yCoxMyO2 cathode materials and their precursor particles via co-precipitation process
Wenbiao Liang, Yin Zhao, Liyi Shi, et al.
Particuology (2023) Vol. 86, pp. 67-85
Open Access | Times Cited: 28
Machine learning promotes the development of all-solid-state batteries
Yong Qiu, Xu Zhang, Yun Tian, et al.
Chinese Journal of Structural Chemistry (2023) Vol. 42, Iss. 9, pp. 100118-100118
Closed Access | Times Cited: 23
Machine learning in energy storage material discovery and performance prediction
Guo-Chang Huang, Fuqiang Huang, Wujie Dong
Chemical Engineering Journal (2024) Vol. 492, pp. 152294-152294
Closed Access | Times Cited: 16
Co-free and low strain cathode materials for sodium-ion batteries: Machine learning-based materials discovery
Minseon Kim, Woon‐Hong Yeo, Kyoungmin Min
Energy storage materials (2024) Vol. 69, pp. 103405-103405
Closed Access | Times Cited: 11
Machine-Learning-Driven High-Throughput Screening for High-Energy Density and Stable NASICON Cathodes
Jinyoung Jeong, Juo Kim, Jiwon Sun, et al.
ACS Applied Materials & Interfaces (2024) Vol. 16, Iss. 19, pp. 24431-24441
Closed Access | Times Cited: 11
Polymer Tetrabenzimidazole Aluminum Phthalocyanine Complex with Carbon Nanotubes: A Promising Approach for Boosting Lithium-Ion Battery Anode Performance
Keshavananada Prabhu Channabasavana Hundi Puttaningaiah, Shilpa D Ramegowda, Jaehyun Hur
ACS Applied Energy Materials (2024) Vol. 7, Iss. 15, pp. 6793-6806
Closed Access | Times Cited: 11
Machine learning-driven discovery of innovative hybrid solid electrolytes for high-performance all-solid-state batteries
J.-G. Kim, Jiwon Sun, Juo Kim, et al.
Chemical Engineering Journal (2025), pp. 161926-161926
Closed Access | Times Cited: 1
High-energy density ultra-thick drying-free Ni-rich cathode electrodes for application in Lithium-ion batteries
Tom James Embleton, Jae Hong Choi, Sung-Jae Won, et al.
Energy storage materials (2024) Vol. 71, pp. 103542-103542
Open Access | Times Cited: 7
Exploring the Ambient-Temperature Degradation Reactions of PET through Two-Step Machine Learning and High-Throughput Experimentation
Yaxin Wang, Shuyuan Li, Kong Meng, et al.
ACS Sustainable Chemistry & Engineering (2024) Vol. 12, Iss. 14, pp. 5415-5426
Closed Access | Times Cited: 6
Machine learning-assisted design of Al2O3–SiO2 porous ceramics based on few-shot datasets
Zhenhao Sun, Nanyan Hu, Lihua Ke, et al.
Ceramics International (2023) Vol. 49, Iss. 18, pp. 29400-29408
Closed Access | Times Cited: 11
Blending of energy benchmarks models for residential buildings
Gyanesh Gupta, Sanjay Mathur, Jyotirmay Mathur, et al.
Energy and Buildings (2023) Vol. 292, pp. 113195-113195
Closed Access | Times Cited: 11
Machine learning-assisted prediction, screen, and interpretation of porous carbon materials for high-performance supercapacitors
Hongwei Liu, Zhenming Cui, Zhennan Qiao, et al.
Journal of Materials Informatics (2024) Vol. 4, Iss. 4
Open Access | Times Cited: 4
Artificial Intelligence and Li Ion Batteries: Basics and Breakthroughs in Electrolyte Materials Discovery
Haneen Alzamer, Russlan Jaafreh, Jung-Gu Kim, et al.
Crystals (2025) Vol. 15, Iss. 2, pp. 114-114
Open Access
A Comprehensive Review of Thermal Management Methods and Ideal System Design for Improved Electric Vehicle Battery Pack Performance and Safety
M. Murugan, P. V. Elumalai, K. Vijayakumar, et al.
Energy Science & Engineering (2025)
Open Access
Data-driven discovery of vanadium-based anode materials for lithium-ion batteries
Yudi Mo, Zhigang Tang, Long Zheng, et al.
Journal of Energy Storage (2025) Vol. 118, pp. 116290-116290
Closed Access
Mechanically robust and conductive Ti5Te4/P@C composite materials as promising lithium-ion battery anodes
Hye Won Yang, Ji Hyeon Yoo, Jaehyun Hur, et al.
Journal of Energy Storage (2024) Vol. 86, pp. 111218-111218
Closed Access | Times Cited: 3
Machine learning descriptors for crystal materials: applications in Ni-rich layered cathode and lithium anode materials for high-energy-density lithium batteries
Ruiqi Zhang, F. C. Rong, Genming Lai, et al.
Journal of Materials Informatics (2024) Vol. 4, Iss. 4
Open Access | Times Cited: 3
Optimal Surrogate Models for Predicting the Elastic Moduli of Metal–Organic Frameworks via Multiscale Features
Jaejun Lee, Inhyo Lee, Jaejung Park, et al.
Chemistry of Materials (2023) Vol. 35, Iss. 24, pp. 10457-10475
Closed Access | Times Cited: 7
A complete and effective target-based data-driven flow screening for reliable cathode materials for aluminum-ion batteries
Zheng Li, Ruxiang Liu, Chunfang Zhang, et al.
Applied Energy (2024) Vol. 376, pp. 124182-124182
Closed Access | Times Cited: 2
Metaheuristics-guided active learning for optimizing reaction conditions of high-performance methane conversion
Gyoung S. Na, Hyun Woo Kim
Applied Soft Computing (2024) Vol. 164, pp. 111935-111935
Open Access | Times Cited: 1
Artificial intelligence driven design of cathode materials for sodium-ion batteries using graph deep learning method
Kong Meng, Kun Bai, Shaorui Sun
Journal of Energy Storage (2024) Vol. 101, pp. 113809-113809
Closed Access | Times Cited: 1
Chemo-Mechanical Coupling Measurement of LiMn2O4 Composite Electrode during Electrochemical Cycling
Huijie Yu, Jiangtao Li, Hainan Jiang, et al.
Batteries (2023) Vol. 9, Iss. 4, pp. 209-209
Open Access | Times Cited: 2
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