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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:

Data-driven analysis and prediction of stable phases for high-entropy alloy design
Iman Peivaste, Ericmoore Jossou, Ahmed A. Tiamiyu
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 15

Showing 15 citing articles:

Multicomponent alloys design and mechanical response: From high entropy alloys to complex concentrated alloys
Manuel Cabrera, Yovany Oropesa, Juan Pablo Sanhueza, et al.
Materials Science and Engineering R Reports (2024) Vol. 161, pp. 100853-100853
Closed Access | Times Cited: 14
Exploring the limits of Fe content in Al-Co-Cr-Fe-Ni-based eutectic high entropy alloys using CALPHAD-based alloy design
Jithin Joseph, Murugesan Annasamy, Peter Hodgson, et al.
Journal of Alloys and Compounds (2024) Vol. 1002, pp. 175431-175431
Open Access | Times Cited: 8
Machine Learning-driven insights into phase prediction for high entropy alloys
Reliance Jain, Sandeep Jain, Sheetal Kumar Dewangan, et al.
Journal of Alloys and Metallurgical Systems (2024), pp. 100110-100110
Open Access | Times Cited: 8
Data-driven design of high bulk modulus high entropy alloys using machine learning
Sandeep Jain, Reliance Jain, Vinod Kumar, et al.
Journal of Alloys and Metallurgical Systems (2024) Vol. 8, pp. 100128-100128
Open Access | Times Cited: 8
New analytical parameters for B2 phase prediction as a complement to multiclass phase prediction using machine learning in multicomponent alloys: A computational approach with experimental validation
Ángelo Oñate, Herrim Seidou, Jérome Tchoufang-Tchuindjang, et al.
Journal of Alloys and Compounds (2025), pp. 179950-179950
Closed Access
Teaching artificial intelligence to perform rapid, resolution-invariant grain growth modeling via Fourier Neural Operator
Iman Peivaste, A. Makradi, Salim Belouettar
Computer Methods in Applied Mechanics and Engineering (2025) Vol. 440, pp. 117945-117945
Closed Access
Phase Stability and Transitions in High-Entropy Alloys: Insights from Lattice Gas Models, Computational Simulations, and Experimental Validation
Łukasz Łach
Entropy (2025) Vol. 27, Iss. 5, pp. 464-464
Open Access
Research progress in data-driven constitutive modeling of composite solid propellants
Bin Yuan, Hongfu Qiang, Chunguo Yue
AIP Advances (2025) Vol. 15, Iss. 5
Open Access
Dislocation nucleation and shear sliding at dual-phase high-entropy alloy semi-coherent interface with atomic complexity
Jiacheng Zhang, Qian Lei, Wenqing Yang, et al.
Acta Materialia (2025), pp. 121118-121118
Closed Access
Design Approaches of High‐Entropy Alloys Using Artificial Intelligence: A Review
Nour Mahmoud Eldabah, Ayush Pratap, Atul Pandey, et al.
Advanced Engineering Materials (2025)
Closed Access
Machine learning based prediction of Young's modulus of stainless steel coated with high entropy alloys
N. Radhika, M. Sabarinathan, S. Ragunath, et al.
Results in Materials (2024) Vol. 23, pp. 100607-100607
Open Access | Times Cited: 3
Substitutional effects of Al and Mn on the microstructure and mechanical response of Cantor-derived high-entropy alloys for nuclear structural applications
Muyideen Adegbite, Ahmed A. Tiamiyu
Materials Science and Engineering A (2024), pp. 147674-147674
Open Access | Times Cited: 3
Prediction of Mechanical Properties of Rare-Earth Magnesium Alloys Based on Convolutional Neural Networks
Mei Cheng, Xiya Jia, Zhimin Zhang
Materials (2024) Vol. 17, Iss. 20, pp. 4956-4956
Open Access | Times Cited: 2
Machine Learning-Based Prediction of Stability in High-Entropy Nitride Ceramics
Tianyu Lin, Ruolan Wang, Dazhi Liu
Crystals (2024) Vol. 14, Iss. 5, pp. 429-429
Open Access | Times Cited: 1
Improved Machine Learning Framework for Prediction of Phase and Crystal Structure of High Entropy Alloys
Debsundar Dey, Suchandan K Das, Anik Pal, et al.
Journal of Alloys and Metallurgical Systems (2024) Vol. 9, pp. 100144-100144
Closed Access
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