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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.

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Showing 11 citing articles:

Interpretable machine learning-assisted design of Fe-based nanocrystalline alloys with high saturation magnetic induction and low coercivity
Ning Zhang, Aina He, Gan Zhang, et al.
Journal of Material Science and Technology (2024) Vol. 188, pp. 73-83
Closed Access | Times Cited: 12
Design and preparation of novel Fe-Si-B-P-C-Nb amorphous /nanocrystalline alloys guided by machine learning
Yifan He, Rui Sun, S. L. Tang, et al.
Progress in Natural Science Materials International (2025)
Closed Access
Advances in High Magnetic Induction and Low Loss Fe‐Based Nanocrystalline Alloys
C.Y. Liu, Fuyao Yang, Yu Han, et al.
Electrical materials and applications. (2025) Vol. 2, Iss. 1
Open Access
Deep learning-based prediction of saturation magnetic flux density in Fe-based metallic glasses via transfer learning
Chunghee Nam
Materials Chemistry and Physics (2024) Vol. 315, pp. 129076-129076
Closed Access | Times Cited: 4
Predicting the magnetic properties of Fe-based bulk metallic glasses by ensemble machine learning and interpretable information
Qiulin Bi, Jialiang Liu, Junting Li, et al.
Journal of Alloys and Compounds (2025), pp. 178858-178858
Closed Access
Accelerating Finite Element Simulation of Metallic Glasses Using Data-Driven Constitutive Models Trained by Deep Neural Networks
Bo Pang, Zhilin Long, Tao Long, et al.
Materials Today Communications (2025), pp. 112252-112252
Closed Access
Data mining accelerated the design strategy of high‐entropy alloys with the largest hardness based on genetic algorithm optimization
Xianzhe Jin, Hong Luo, Xuefei Wang, et al.
Materials Genome Engineering Advances (2024) Vol. 2, Iss. 2
Open Access | Times Cited: 3
High creep resistance in amorphous/crystalline dual-phase nanostructured CoCrFeNiMn high entropy alloys
Lili Xiao, Kai Sun, Wengao Pan, et al.
Journal of Materials Research and Technology (2024) Vol. 33, pp. 5136-5141
Open Access | Times Cited: 1
Machine learning assisted design and preparation of Fe85Si2B8.5P3.5C1 amorphous/nanocrystalline alloy with high B and low H
Shihao Tang, Rui Sun, Yifan He, et al.
Materials & Design (2024), pp. 113461-113461
Open Access | Times Cited: 1
High Creep Resistance in Amorphous/Crystalline Dual-Phase Nanostructured Cocrfenimn High Entropy Alloys
Lili Xiao, Wengao Pan, Kejia Kang, et al.
(2024)
Closed Access
Generative model-based inverse design of Fe-based metallic glasses with high saturation magnetic flux density
K.Y. Li, L.C. Liu, Lin Shao, et al.
Journal of Alloys and Compounds (2024), pp. 178325-178325
Closed Access
Machine Learning Application for Functional Properties Prediction in Magnetic Materials
V. A. Milyutin, N. N. Nikulchenkov
The Physics of Metals and Metallography (2024) Vol. 125, Iss. 12, pp. 1351-1366
Closed Access
Deep Learning-Based Prediction of Saturation Magnetic Flux Density in Fe-Based Metallic Glasses Via Transfer Learning
Chunghee Nam
(2023)
Closed Access
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