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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:

A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning
Cameron J. Hargreaves, Michael W. Gaultois, Luke M. Daniels, et al.
npj Computational Materials (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 37

Showing 1-25 of 37 citing articles:

Machine Learning‐Assisted Property Prediction of Solid‐State Electrolyte
Jin Li, Meisa Zhou, Hong‐Hui Wu, et al.
Advanced Energy Materials (2024) Vol. 14, Iss. 20
Closed Access | Times Cited: 51
Development of solid polymer electrolytes for solid-state lithium battery applications
Jieyan Li, Xin Chen, Saz Muhammad, et al.
Materials Today Energy (2024) Vol. 43, pp. 101574-101574
Closed Access | Times Cited: 21
Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials
Bohayra Mortazavi
Advanced Energy Materials (2024)
Open Access | Times Cited: 21
Machine learning-accelerated discovery and design of electrode materials and electrolytes for lithium ion batteries
Guangsheng Xu, Mingxi Jiang, Jinliang Li, et al.
Energy storage materials (2024) Vol. 72, pp. 103710-103710
Closed Access | Times Cited: 19
Sustainable Extraction of Critical Minerals from Waste Batteries: A Green Solvent Approach in Resource Recovery
Afzal Ahmed Dar, Zhi Chen, Gaixia Zhang, et al.
Batteries (2025) Vol. 11, Iss. 2, pp. 51-51
Open Access | Times Cited: 2
Application-oriented design of machine learning paradigms for battery science
Ying Wang
npj Computational Materials (2025) Vol. 11, Iss. 1
Open Access | Times Cited: 2
Oxide Ion-Conducting Materials Containing Tetrahedral Moieties: Structures and Conduction Mechanisms
Xiaoyan Yang, Alberto J. Fernández–Carrión, Xiaojun Kuang
Chemical Reviews (2023) Vol. 123, Iss. 15, pp. 9356-9396
Closed Access | Times Cited: 36
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
Diffusion mechanisms of fast lithium-ion conductors
KyuJung Jun, Yu Chen, Grace Wei, et al.
Nature Reviews Materials (2024)
Closed Access | Times Cited: 15
Progress of machine learning in materials design for Li-Ion battery
Prasshanth C.V., Arun Kumar Lakshminarayanan, R. Brindha, et al.
Next Materials (2024) Vol. 2, pp. 100145-100145
Open Access | Times Cited: 10
Improving ionic conductivity of garnet solid-state electrolytes using Gradient boosting regression optimized machine learning
Yue Ma, Shaoxiong Han, Yan Sun, et al.
Journal of Power Sources (2024) Vol. 604, pp. 234492-234492
Closed Access | Times Cited: 9
Recent Applications of Theoretical Calculations and Artificial Intelligence in Solid-State Electrolyte Research: A Review
Ming-Wei Wu, Zheng Wei, Yan Zhao, et al.
Nanomaterials (2025) Vol. 15, Iss. 3, pp. 225-225
Open Access | Times Cited: 1
Data-driven analysis and visualization of dielectric properties curated from scientific literature
Tomoki Murata, Noriko Saito, Eiji Koyama, et al.
Science and Technology of Advanced Materials Methods (2025)
Open Access | Times Cited: 1
Evaluation of solid electrolytes: Development of conventional and interdisciplinary approaches
Muhammad Khurram Tufail, Pengbo Zhai, Waquar Khokar, et al.
Interdisciplinary materials (2023) Vol. 2, Iss. 4, pp. 529-568
Open Access | Times Cited: 17
Speeding up the development of solid state electrolyte by machine learning
Qianyu Hu, Kunfeng Chen, J. Y. Li, et al.
Next Energy (2024) Vol. 5, pp. 100159-100159
Open Access | Times Cited: 6
Atom substitution of the solid-state electrolyte Li10GeP2S12 for stabilized all-solid-state lithium metal batteries
Zijing Wan, Xiaozhen Chen, Ziqi Zhou, et al.
Journal of Energy Chemistry (2023) Vol. 88, pp. 28-38
Closed Access | Times Cited: 13
Machine Learning in Lithium-Ion Battery: Applications, Challenges, and Future Trends
Alireza Valizadeh, Mohammad Hossein Amirhosseini
SN Computer Science (2024) Vol. 5, Iss. 6
Open Access | Times Cited: 4
Ionic Conductivity Study of Antiperovskite Solid-State Electrolytes Based on Interpretable Machine Learning
Shang Xiang, Shaowen Lu, Jiawei Li, et al.
ACS Applied Energy Materials (2025)
Closed Access
Leveraging Prompt Engineering in Large Language Models for Accelerating Chemical Research
Feifei Luo, Jinglang Zhang, Qilong Wang, et al.
ACS Central Science (2025) Vol. 11, Iss. 4, pp. 511-519
Open Access
Data-driven design of electrolyte additives supporting high-performance 5 V LiNi0.5Mn1.5O4 positive electrodes
Bingning Wang, Hieu A. Doan, Seoung‐Bum Son, et al.
Nature Communications (2025) Vol. 16, Iss. 1
Open Access
Discovery of Crystalline Inorganic Solids in the Digital Age
Dmytro Antypov, Andrij Vasylenko, Christopher M. Collins, et al.
Accounts of Chemical Research (2025)
Open Access
Compositional machine learning and high-throughput screening aided discovery of novel anti-perovskite solid-state electrolytes
C. X. Lin, Lin Zhang, Yi Dong
Journal of Energy Storage (2025) Vol. 125, pp. 116990-116990
Closed Access
Benchmarking machine learning models for predicting lithium ion migration
Artem D. Dembitskiy, Innokentiy S. Humonen, Roman A. Eremin, et al.
npj Computational Materials (2025) Vol. 11, Iss. 1
Open Access
Inferring energy–composition relationships with Bayesian optimization enhances exploration of inorganic materials
Andrij Vasylenko, Benjamin M. Asher, Christopher M. Collins, et al.
The Journal of Chemical Physics (2024) Vol. 160, Iss. 5
Open Access | Times Cited: 3
Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility
Ryan Jacobs, Lane E. Schultz, Aristana Scourtas, et al.
Machine Learning Science and Technology (2024) Vol. 5, Iss. 4, pp. 045051-045051
Open Access | Times Cited: 3
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