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:

Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature
Mengwei Wu, Wei Yong, Cunqin Fu, et al.
International Journal of Minerals Metallurgy and Materials (2024) Vol. 31, Iss. 4, pp. 773-785
Closed Access | Times Cited: 5

Showing 5 citing articles:

Designing Laves-phase RFe2-type alloy with excellent magnetostrictive performance by physics-informed interpretable machine learning
Pengqiang Hu, Chao Zhou, Ruisheng Zhang, et al.
Materials & Design (2025) Vol. 252, pp. 113799-113799
Open Access | Times Cited: 1

Predictive modeling of phase transformation temperatures in NiTiCu shape memory alloys: Integrating electronic factors through artificial neural network
Rajeshkannan Radhamani, B. Muralidharan
Materials Today Communications (2024) Vol. 38, pp. 108380-108380
Closed Access | Times Cited: 4

Developing an atmospheric aging evaluation model of acrylic coatings: A semi-supervised machine learning algorithm
Yiran Li, Zhongheng Fu, Xiangyang Yu, et al.
International Journal of Minerals Metallurgy and Materials (2024) Vol. 31, Iss. 7, pp. 1617-1627
Closed Access | Times Cited: 1

Giant reversible barocaloric effects with high thermal cycle stability in epoxybonded (MnCoGe)0.96(CuCoSn)0.04 composite
Kuang Ya-fei, Kun Tao, Bo Yang, et al.
International Journal of Minerals Metallurgy and Materials (2024)
Closed Access

Classified dataset, regression and machine learning modeling for prediction of phase transformation temperatures in steels
Jinlei Lu, Guanglong Xu, Fuwen Chen, et al.
Calphad (2024) Vol. 87, pp. 102748-102748
Closed Access

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