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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 Critical Review of Machine Learning Techniques on Thermoelectric Materials
Xiangdong Wang, Ye Sheng, Jinyan Ning, et al.
The Journal of Physical Chemistry Letters (2023) Vol. 14, Iss. 7, pp. 1808-1822
Closed Access | Times Cited: 33

Showing 1-25 of 33 citing articles:

Half-Heusler thermoelectrics: Advances from materials fundamental to device engineering
Wenjie Li, Subrata Ghosh, Na Liu, et al.
Joule (2024) Vol. 8, Iss. 5, pp. 1274-1311
Closed Access | Times Cited: 28
High‐Throughput Strategies in the Discovery of Thermoelectric Materials
Tingting Deng, Pengfei Qiu, Tingwei Yin, et al.
Advanced Materials (2024) Vol. 36, Iss. 13
Closed Access | Times Cited: 25
SnSe: The rise of the ultrahigh thermoelectric performance material
Taeshik Kim, Hyungseok Lee, In Jae Chung
Bulletin of the Korean Chemical Society (2024) Vol. 45, Iss. 3, pp. 186-199
Closed Access | Times Cited: 12
Dealing with the big data challenges in AI for thermoelectric materials
Xue Jia, Alex Aziz, Yusuke Hashimoto, et al.
Science China Materials (2024) Vol. 67, Iss. 4, pp. 1173-1182
Closed Access | Times Cited: 10
High-Entropy Engineering in Thermoelectric Materials: A Review
Subrata Ghosh, Lavanya Raman, Soumya Sridar, et al.
Crystals (2024) Vol. 14, Iss. 5, pp. 432-432
Open Access | Times Cited: 7
Machine learning assisted adsorption performance evaluation of biochar on heavy metal
Qiannan Duan, Pengwei Yan, Yichen Feng, et al.
Frontiers of Environmental Science & Engineering (2024) Vol. 18, Iss. 5
Closed Access | Times Cited: 6
Recent advances in machine learning interatomic potentials for cross-scale computational simulation of materials
Nian Ran, Liang Yin, Wujie Qiu, et al.
Science China Materials (2024) Vol. 67, Iss. 4, pp. 1082-1100
Closed Access | Times Cited: 4
Interpretable Machine Learning Model on Thermal Conductivity Using Publicly Available Datasets and Our Internal Lab Dataset
Nikhil K. Barua, E. L. Hall, Yifei Cheng, et al.
Chemistry of Materials (2024) Vol. 36, Iss. 14, pp. 7089-7100
Closed Access | Times Cited: 4
Process optimization on kesterite-based ceramics for enhancing their thermoelectric performances assisted by active machine learning approach: A tool for metal-sulfide ceramics development
Cédric Bourgès, G. Lambard, Naoki Sato, et al.
Acta Materialia (2024) Vol. 281, pp. 120342-120342
Open Access | Times Cited: 4
The application of machine learning in 3D/4D printed stimuli-responsive hydrogels
Onome Ejeromedoghene, Moses Kumi, Ephraim Akor, et al.
Advances in Colloid and Interface Science (2024) Vol. 336, pp. 103360-103360
Closed Access | Times Cited: 4
The Design of Intrinsically Conductive Metal‐Organic Frameworks for Thermoelectric Materials
Molly McVea, Christian B. Nielsen, Oliver Fenwick, et al.
Small Science (2025)
Open Access
Solid-State Synthesis Enables Enhanced Crystallinity and Tunable Optical Properties in Lanthanum Oxytelluride
Melissa Orr, Hoa H. Nguyen, Thomas S. Ie, et al.
Inorganic Chemistry (2025)
Closed Access
Highly Deformable Van der Waals Chalcogenides with Superior Thermoelectric Performance from Interpretable Machine Learning
Qi Ren, Bonan Zhu, Gang Tang, et al.
Small (2025)
Closed Access
Heat pipe-cooled reactors: A comprehensive review of evolution, challenges, research status, and outlook
Zeqin Zhang, Zhipeng Zhang, Chenglong Wang, et al.
Renewable and Sustainable Energy Reviews (2025) Vol. 213, pp. 115486-115486
Closed Access
Exploiting chemical bonding principles to design high-performance thermoelectric materials
Anthony V. Powell, Paz Vaqueiro, Sahil Tippireddy, et al.
Nature Reviews Chemistry (2025)
Closed Access
Advancements in Distributed Transport Property Thermoelectrics: Enhancing Performance through Material Selection and Innovative Manufacturing
Doug Crane, Lon E. Bell
Solid State Sciences (2025), pp. 107908-107908
Closed Access
Large language model-driven database for thermoelectric materials
Suman Itani, Yibo Zhang, Jiadong Zang
Computational Materials Science (2025) Vol. 253, pp. 113855-113855
Closed Access
Machine-learning-assisted discovery of 212-Zintl-phase compounds with ultra-low lattice thermal conductivity
Qi Ren, Dali Chen, Lixiang Rao, et al.
Journal of Materials Chemistry A (2023) Vol. 12, Iss. 2, pp. 1157-1165
Closed Access | Times Cited: 8
Machine Learning Combined with Weighted Voting Regression and Proactive Searching Progress to Discover ABO3-δ Perovskites with High Oxide Ionic Conductivity
Pengcheng Xu, Tian Lu, Xiaobo Ji, et al.
The Journal of Physical Chemistry C (2023) Vol. 127, Iss. 34, pp. 17096-17108
Closed Access | Times Cited: 7
Unleashing the power of artificial intelligence in phonon thermal transport: Current challenges and prospects
Ming Hu
Journal of Applied Physics (2024) Vol. 135, Iss. 17
Open Access | Times Cited: 2
A guide to discovering next-generation semiconductor materials using atomistic simulations and machine learning
Arun Mannodi‐Kanakkithodi
Computational Materials Science (2024) Vol. 243, pp. 113108-113108
Closed Access | Times Cited: 2
Discovering ABO3-type perovskite with different dielectric constants via intelligent optimization algorithm
Taizhong Yao, Lanping Chen, Nan Hu, et al.
AIP Advances (2024) Vol. 14, Iss. 7
Open Access | Times Cited: 2
A review on machine learning-guided design of energy materials
Seongmin Kim, Jiaxin Xu, Wenjie Shang, et al.
Progress in Energy (2024) Vol. 6, Iss. 4, pp. 042005-042005
Closed Access | Times Cited: 1
Machine Learning for Next Generation Thermoelectrics
Kıvanç Sağlık, Siddharth Srinivasan, V Petrov Victor, et al.
Materials Today Energy (2024), pp. 101700-101700
Closed Access | Times Cited: 1
Machine Learning and First-Principle Predictions of Materials with Low Lattice Thermal Conductivity
Chia-Min Lin, Abishek Khatri, Da Yan, et al.
Materials (2024) Vol. 17, Iss. 21, pp. 5372-5372
Open Access | Times Cited: 1
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