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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:

Efficient machine-learning model for fast assessment of elastic properties of high-entropy alloys
Guillermo Vazquez, Prashant Singh, Daniel Sauceda, et al.
Acta Materialia (2022) Vol. 232, pp. 117924-117924
Open Access | Times Cited: 70

Showing 1-25 of 70 citing articles:

Bayesian optimization with active learning of design constraints using an entropy-based approach
Danial Khatamsaz, Brent Vela, Prashant Singh, et al.
npj Computational Materials (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 60
Design of refractory multi-principal-element alloys for high-temperature applications
Gaoyuan Ouyang, Prashant Singh, Ranran Su, et al.
npj Computational Materials (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 30
Effect of stacking fault energy on the thickness and density of annealing twins in recrystallized FCC medium and high-entropy alloys
Mike Schneider, Jean‐Philippe Couzinié, Amin Shalabi, et al.
Scripta Materialia (2023) Vol. 240, pp. 115844-115844
Open Access | Times Cited: 30
Knowledge-aware design of high-strength aviation aluminum alloys via machine learning
Juan Yong-fei, Guoshuai Niu, Yang Yang, et al.
Journal of Materials Research and Technology (2023) Vol. 24, pp. 346-361
Open Access | Times Cited: 29
A theoretical and deep learning hybrid model for predicting surface roughness of diamond-turned polycrystalline materials
Chunlei He, Jiwang Yan, Shuqi Wang, et al.
International Journal of Extreme Manufacturing (2023) Vol. 5, Iss. 3, pp. 035102-035102
Open Access | Times Cited: 28
Methods, progresses, and opportunities of materials informatics
Chen Li, Kun Zheng
InfoMat (2023) Vol. 5, Iss. 8
Open Access | Times Cited: 28
High-throughput exploration of the WMoVTaNbAl refractory multi-principal-element alloys under multiple-property constraints
Brent Vela, Cafer Acemi, Prashant Singh, et al.
Acta Materialia (2023) Vol. 248, pp. 118784-118784
Open Access | Times Cited: 27
Rational design of high-entropy ceramics based on machine learning – A critical review
Jun Zhang, Xuepeng Xiang, Biao Xu, et al.
Current Opinion in Solid State and Materials Science (2023) Vol. 27, Iss. 2, pp. 101057-101057
Closed Access | Times Cited: 26
A review on high-throughput development of high-entropy alloys by combinatorial methods
Shahryar Mooraj, Wen Chen
Journal of Materials Informatics (2023) Vol. 3, Iss. 1, pp. 4-4
Open Access | Times Cited: 26
Toward ultra-high strength high entropy alloys via feature engineering
Yan Zhang, Cheng Wen, Pengfei Dang, et al.
Journal of Material Science and Technology (2024) Vol. 200, pp. 243-252
Closed Access | Times Cited: 14
A review on recent progress of refractory high entropy alloys: from fundamental research to engineering applications
Longchao Zhuo, Yixing Xie, Bingqing Chen
Journal of Materials Research and Technology (2024)
Open Access | Times Cited: 14
Fatigue life prediction of the FCC-based multi-principal element alloys via domain knowledge-based machine learning
Xiao Lu, Gang Wang, Weimin Long, et al.
Engineering Fracture Mechanics (2024) Vol. 296, pp. 109860-109860
Closed Access | Times Cited: 11
Composition Design Strategy for High Entropy Amorphous Alloys
Hongyu Ding, Qi Zhang, Kefu Yao
Materials (2024) Vol. 17, Iss. 2, pp. 453-453
Open Access | Times Cited: 10
Alloying Effects on the Transport Properties of Refractory High-entropy Alloys
Prashant Singh, Cafer Acemi, Aditya Kuchibhotla, et al.
Acta Materialia (2024) Vol. 276, pp. 120032-120032
Closed Access | Times Cited: 10
An equivariant graph neural network for the elasticity tensors of all seven crystal systems
Mingjian Wen, Matthew K. Horton, Jason M. Munro, et al.
Digital Discovery (2024) Vol. 3, Iss. 5, pp. 869-882
Open Access | Times Cited: 9
Machine learning studies for magnetic compositionally complex alloys: A critical review
Xin Li, C.H. Shek, Peter K. Liaw, et al.
Progress in Materials Science (2024) Vol. 146, pp. 101332-101332
Closed Access | Times Cited: 8
A comparative study of predicting high entropy alloy phase fractions with traditional machine learning and deep neural networks
Shusen Liu, Brandon Bocklund, James Diffenderfer, et al.
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 8
Developing softening-resistant Cu-Cr alloys and understanding their mechanisms via mechanism-informed interpretable machine learning
Muzhi Ma, Zhou Li, Yuyuan Zhao, et al.
Journal of Material Science and Technology (2025)
Closed Access | Times Cited: 1
Interpretable machine learning workflow for evaluation of the transformation temperatures of TiZrHfNiCoCu high entropy shape memory alloys
Shiyu He, Yanming Wang, Zhengyang Zhang, et al.
Materials & Design (2022) Vol. 225, pp. 111513-111513
Open Access | Times Cited: 33
Machine learning accelerated design of non-equiatomic refractory high entropy alloys based on first principles calculation
Yu Gao, Songsong Bai, Kai Chong, et al.
Vacuum (2022) Vol. 207, pp. 111608-111608
Closed Access | Times Cited: 32
A machine learning framework for elastic constants predictions in multi-principal element alloys
Nathan Linton, Dilpuneet S. Aidhy
APL Machine Learning (2023) Vol. 1, Iss. 1
Open Access | Times Cited: 17
Heat-resistant aluminum alloy design using explainable machine learning
Jinxian Huang, Daisuke Ando, Yuji Sutou
Materials & Design (2024) Vol. 243, pp. 113057-113057
Open Access | Times Cited: 6
Machine learning-assisted design of high-entropy alloys with superior mechanical properties
Jianye He, Zezhou Li, Pingluo Zhao, et al.
Journal of Materials Research and Technology (2024) Vol. 33, pp. 260-286
Open Access | Times Cited: 6
Efficient first principles based modeling via machine learning: from simple representations to high entropy materials
Kangming Li, Kamal Choudhary, Brian DeCost, et al.
Journal of Materials Chemistry A (2024) Vol. 12, Iss. 21, pp. 12412-12422
Open Access | Times Cited: 5
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