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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:

Machine learning assisted materials design and discovery for rechargeable batteries
Yue Liu, Biru Guo, Xinxin Zou, et al.
Energy storage materials (2020) Vol. 31, pp. 434-450
Closed Access | Times Cited: 325

Showing 1-25 of 325 citing articles:

Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm
Tanveer Ahmad, Rafał Madoński, Dongdong Zhang, et al.
Renewable and Sustainable Energy Reviews (2022) Vol. 160, pp. 112128-112128
Closed Access | Times Cited: 325
Applying Classical, Ab Initio, and Machine-Learning Molecular Dynamics Simulations to the Liquid Electrolyte for Rechargeable Batteries
Nan Yao, Xiang Chen, Zhongheng Fu, et al.
Chemical Reviews (2022) Vol. 122, Iss. 12, pp. 10970-11021
Closed Access | Times Cited: 274
Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries
Chade Lv, Xin Zhou, Lixiang Zhong, et al.
Advanced Materials (2021) Vol. 34, Iss. 25
Open Access | Times Cited: 249
State of Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using Machine Learning Algorithms
Chandran Venkatesan, Chandrashekhar K. Patil, Alagar Karthick, et al.
World Electric Vehicle Journal (2021) Vol. 12, Iss. 1, pp. 38-38
Open Access | Times Cited: 238
2021 roadmap for sodium-ion batteries
Nuria Tapia‐Ruiz, A. Robert Armstrong, Hande Alptekin, et al.
Journal of Physics Energy (2021) Vol. 3, Iss. 3, pp. 031503-031503
Open Access | Times Cited: 210
Aligning artificial intelligence with climate change mitigation
Lynn H. Kaack, Priya L. Donti, Emma Strubell, et al.
Nature Climate Change (2022) Vol. 12, Iss. 6, pp. 518-527
Closed Access | Times Cited: 208
A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness
Yang Chen, Chang Ren, Yuefei Jia, et al.
Acta Materialia (2021) Vol. 222, pp. 117431-117431
Closed Access | Times Cited: 183
Tailoring the structure of silicon-based materials for lithium-ion batteries via electrospinning technology
Aoming Huang, Yanchen Ma, Jian Peng, et al.
eScience (2021) Vol. 1, Iss. 2, pp. 141-162
Open Access | Times Cited: 174
Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage
Daniel Rangel-Martinez, K.D.P. Nigam, Luis Ricardez–Sandoval
Process Safety and Environmental Protection (2021) Vol. 174, pp. 414-441
Closed Access | Times Cited: 172
Machine learning for advanced energy materials
Liu Yun, Oladapo Christopher Esan, Zhefei Pan, et al.
Energy and AI (2021) Vol. 3, pp. 100049-100049
Open Access | Times Cited: 145
Accelerating materials discovery using machine learning
Yongfei Juan, Yongbing Dai, Yang Yang, et al.
Journal of Material Science and Technology (2020) Vol. 79, pp. 178-190
Closed Access | Times Cited: 139
Reviewing machine learning of corrosion prediction in a data-oriented perspective
Leonardo Bertolucci Coelho, Dawei Zhang, Yves Van Ingelgem, et al.
npj Materials Degradation (2022) Vol. 6, Iss. 1
Open Access | Times Cited: 134
Innovative Materials Science via Machine Learning
Chaochao Gao, Xin Min, Minghao Fang, et al.
Advanced Functional Materials (2021) Vol. 32, Iss. 1
Closed Access | Times Cited: 128
A review of the recent progress in battery informatics
Chen Ling
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 128
Generative artificial intelligence and its applications in materials science: Current situation and future perspectives
Yue Liu, Zhengwei Yang, Zhenyao Yu, et al.
Journal of Materiomics (2023) Vol. 9, Iss. 4, pp. 798-816
Open Access | Times Cited: 121
Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale
Xiang Chen, Xinyan Liu, Xin Shen, et al.
Angewandte Chemie International Edition (2021) Vol. 60, Iss. 46, pp. 24354-24366
Closed Access | Times Cited: 117
Data‐Driven Materials Innovation and Applications
Zhuo Wang, Zhehao Sun, Hang Yin, et al.
Advanced Materials (2022) Vol. 34, Iss. 36
Closed Access | Times Cited: 97
Nonaqueous rechargeable aluminum batteries
Kok Long Ng, Brohath Amrithraj, Gisele Azimi
Joule (2022) Vol. 6, Iss. 1, pp. 134-170
Open Access | Times Cited: 96
Recent advances in manipulating strategy of aqueous electrolytes for Zn anode stabilization
Haoyu Li, Shaohua Guo, Haoshen Zhou
Energy storage materials (2023) Vol. 56, pp. 227-257
Closed Access | Times Cited: 91
Remaining life prediction of lithium-ion batteries based on health management: A review
Kai Song, Die Hu, Yao Tong, et al.
Journal of Energy Storage (2022) Vol. 57, pp. 106193-106193
Closed Access | Times Cited: 73
Machine learning in energy storage materials
Zhonghui Shen, Hanxing Liu, Yang Shen, et al.
Interdisciplinary materials (2022) Vol. 1, Iss. 2, pp. 175-195
Open Access | Times Cited: 68
Data-Driven Insight into the Reductive Stability of Ion–Solvent Complexes in Lithium Battery Electrolytes
Yuchen Gao, Nan Yao, Xiang Chen, et al.
Journal of the American Chemical Society (2023) Vol. 145, Iss. 43, pp. 23764-23770
Closed Access | Times Cited: 67
Machine‐Learning‐Assisted Nanozyme Design: Lessons from Materials and Engineered Enzymes
Jie Zhuang, Adam C. Midgley, Yonghua Wei, et al.
Advanced Materials (2023) Vol. 36, Iss. 10
Closed Access | Times Cited: 62
Optimization of elliptical pin-fin microchannel heat sink based on artificial neural network
Chenyang Yu, Xu Zhu, Zhigang Li, et al.
International Journal of Heat and Mass Transfer (2023) Vol. 205, pp. 123928-123928
Closed Access | Times Cited: 52
Scope of machine learning in materials research—A review
Md Hosne Mobarak, Mariam Akter Mimona, Md Aminul Islam, et al.
Applied Surface Science Advances (2023) Vol. 18, pp. 100523-100523
Open Access | Times Cited: 49
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