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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:

Designing high elastic modulus magnesium-based composite materials via machine learning approach
Zhu Zhi-hong, Wenhang Ning, Xuanyang Niu, et al.
Materials Today Communications (2023) Vol. 37, pp. 107249-107249
Open Access | Times Cited: 11

Showing 11 citing articles:

Optimizing 3D printed diamond lattice structure and investigating the influence of process parameters on their mechanical integrity using nature-inspired machine learning algorithms
Kaustubh Dwivedi, Shreya Joshi, Rithvik Nair, et al.
Materials Today Communications (2024) Vol. 38, pp. 108233-108233
Closed Access | Times Cited: 10
Machine learning approach for surface morphology of nanoparticles: Image analysis and data mining techniques
Sorabh Lakhanpal, Abhishek Joshi, Kshama Sharma, et al.
AIP conference proceedings (2025) Vol. 3157, pp. 080013-080013
Closed Access
Using Machine Learning Methods to Predict the Ductile-to-Brittle Transition Temperature Shift in RPV Steel Under Different Pulse Current Parameters
Yating Zhang, Biqian Li, Li Shu, et al.
Acta Metallurgica Sinica (English Letters) (2025)
Closed Access
Optimized design of composition and brazing process for Cu-Ag-Zn-Mn-Ni-Si-B-P alloy brazing material based on machine learning strategy to improve brazing properties
Jiheng Fang, Ming Xie, Jiming Zhang, et al.
Materials Today Communications (2024) Vol. 39, pp. 109317-109317
Closed Access | Times Cited: 3
Synthesis, characterization and evaluation of experimental dental composite resin modified by grapefruit seed extract-mediated TiO₂ nanoparticles: green approach
Dina Ezzat, Mai Samy Sheta, El‐Refaie Kenawy, et al.
Odontology (2025)
Open Access
Machine learning-assisted interfacial modulation and configuration design of metal matrix composites: A review
Yangyang Cheng, Rui Shu, Hongliang Sun, et al.
Materials Today Communications (2025), pp. 112504-112504
Closed Access
The Data-Driven Performance Prediction of Lattice Structures: The State-of-the-Art in Properties, Future Trends, and Challenges
Siyuan Yang, Ning Dai, Qianfeng Cao
Aerospace (2025) Vol. 12, Iss. 5, pp. 390-390
Open Access
Exploring shear nonlinearity of plain-woven composites at various temperatures based on machine learning
Jindi Zhou, Kai Huang, Tao Zheng, et al.
Composite Structures (2024) Vol. 346, pp. 118434-118434
Closed Access | Times Cited: 3
Optimization of cooling rate of Q-P treated 42SiCr steel using AI digital twinning
Omid Khalaj, Parsa Hassas, Bohuslav Mašek, et al.
Heliyon (2024) Vol. 10, Iss. 11, pp. e32101-e32101
Open Access | Times Cited: 1
Machine Learning-Based Research on Tensile Strength of SiC-Reinforced Magnesium Matrix Composites via Stir Casting
Zhu Zhi-hong, Wenhang Ning, Xuanyang Niu, et al.
Acta Metallurgica Sinica (English Letters) (2024) Vol. 37, Iss. 3, pp. 453-466
Closed Access
Machine learning accelerated the prediction of mechanical properties of copper modified by TMDs based on molecular dynamics simulation
Guoqing Wang, Ben Gao, Gai Zhao, et al.
Physica Scripta (2024) Vol. 99, Iss. 9, pp. 095930-095930
Closed Access
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