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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:

Multi‐Layer Feature Selection Incorporating Weighted Score‐Based Expert Knowledge toward Modeling Materials with Targeted Properties
Yue Liu, Junming Wu, Maxim Avdeev, et al.
Advanced Theory and Simulations (2020) Vol. 3, Iss. 2
Open Access | Times Cited: 97

Showing 1-25 of 97 citing articles:

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
Explainable machine learning in materials science
Xiaoting Zhong, Brian Gallagher, Shusen Liu, et al.
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 140
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
Data quantity governance for machine learning in materials science
Yue Liu, Zhengwei Yang, Xinxin Zou, et al.
National Science Review (2023) Vol. 10, Iss. 7
Open Access | Times Cited: 70
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
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
Data‐Driven Safety Risk Prediction of Lithium‐Ion Battery
Yikai Jia, Jiani Li, Chunhao Yuan, et al.
Advanced Energy Materials (2021) Vol. 11, Iss. 18
Closed Access | Times Cited: 83
Designed high-performance lithium-ion battery electrodes using a novel hybrid model-data driven approach
Xinlei Gao, Xinhua Liu, Rong He, et al.
Energy storage materials (2021) Vol. 36, pp. 435-458
Closed Access | Times Cited: 77
Bayesian optimization with adaptive surrogate models for automated experimental design
Bowen Lei, Tanner Kirk, Anirban Bhattacharya, et al.
npj Computational Materials (2021) Vol. 7, Iss. 1
Open Access | Times Cited: 62
Machine learning for battery research
Zheng Wei, Qiu He, Yan Zhao
Journal of Power Sources (2022) Vol. 549, pp. 232125-232125
Closed Access | Times Cited: 61
Application of machine learning for advanced material prediction and design
Cheuk Hei Chan, Mingzi Sun, Bolong Huang
EcoMat (2022) Vol. 4, Iss. 4
Open Access | Times Cited: 57
Systematic analysis of the impact of slurry coating on manufacture of Li-ion battery electrodes via explainable machine learning
Mona Faraji Niri, C. D. Reynolds, Luis A. Román‐Ramírez, et al.
Energy storage materials (2022) Vol. 51, pp. 223-238
Open Access | Times Cited: 44
Feature selection method reducing correlations among features by embedding domain knowledge
Yue Liu, Xinxin Zou, Shu-Chang Ma, et al.
Acta Materialia (2022) Vol. 238, pp. 118195-118195
Closed Access | Times Cited: 43
Feature Selection in Machine Learning for Perovskite Materials Design and Discovery
Junya Wang, Pengcheng Xu, Xiaobo Ji, et al.
Materials (2023) Vol. 16, Iss. 8, pp. 3134-3134
Open Access | Times Cited: 22
Machine Learning Guided Dopant Selection for Metal Oxide‐Based Photoelectrochemical Water Splitting: The Case Study of Fe2O3and CuO
Zhiliang Wang, Yuang Gu, Lingxia Zheng, et al.
Advanced Materials (2021) Vol. 34, Iss. 10
Closed Access | Times Cited: 47
Machine Learning Boosting the Development of Advanced Lithium Batteries
Yangting Liu, Qian Zhou, Guanglei Cui
Small Methods (2021) Vol. 5, Iss. 8
Closed Access | Times Cited: 44
Machine learning of materials design and state prediction for lithium ion batteries
Jiale Mao, Jiazhi Miao, Yingying Lü, et al.
Chinese Journal of Chemical Engineering (2021) Vol. 37, pp. 1-11
Closed Access | Times Cited: 41
A Machine Learning Shortcut for Screening the Spinel Structures of Mg/Zn Ion Battery Cathodes with a High Conductivity and Rapid Ion Kinetics
Junfei Cai, Zhilong Wang, Sicheng Wu, et al.
Energy storage materials (2021) Vol. 42, pp. 277-285
Closed Access | Times Cited: 40
Recent progress in the machine learning-assisted rational design of alloys
Huadong Fu, Hong‐Tao Zhang, Changsheng Wang, et al.
International Journal of Minerals Metallurgy and Materials (2022) Vol. 29, Iss. 4, pp. 635-644
Closed Access | Times Cited: 35
Machine learning based approach for phase prediction in high entropy borides
Rahul Mitra, Anurag Bajpai, Krishanu Biswas
Ceramics International (2022) Vol. 48, Iss. 12, pp. 16695-16706
Closed Access | Times Cited: 34
Determination of the heat capacity of cellulosic biosamples employing diverse machine learning approaches
Mohsen Karimi, Marzieh Khosravi, Reza Fathollahi, et al.
Energy Science & Engineering (2022) Vol. 10, Iss. 6, pp. 1925-1939
Open Access | Times Cited: 33
Designing Two-Dimensional Halide Perovskites Based on High-Throughput Calculations and Machine Learning
Wenguang Hu, Lei Zhang, Zhengwei Pan
ACS Applied Materials & Interfaces (2022) Vol. 14, Iss. 18, pp. 21596-21604
Closed Access | Times Cited: 29
Machine learning molecular dynamics simulation identifying weakly negative effect of polyanion rotation on Li-ion migration
Zhenming Xu, Huiyu Duan, Zhi Dou, et al.
npj Computational Materials (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 16
Machine learning strategies for small sample size in materials science
Qiuling Tao, Jinxin Yu, Xiangyu Mu, et al.
Science China Materials (2025)
Closed Access
Monitoring process parameters and predicting rail steel welded joint microstructure and mechanical property of three-wire fusion nozzle electroslag welding
Shengfu Yu, Yang Wang, Zhongyi Zhang
Welding in the World (2025)
Closed Access
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