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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:

Machine learning approach to predict new multiphase high entropy alloys
Yegi Vamsi Krishna, Ujjawal Kumar Jaiswal, M.R. Rahul
Scripta Materialia (2021) Vol. 197, pp. 113804-113804
Closed Access | Times Cited: 110

Showing 1-25 of 110 citing articles:

Machine-learning and high-throughput studies for high-entropy materials
E‐Wen Huang, Wen‐Jay Lee, Sudhanshu S. Singh, et al.
Materials Science and Engineering R Reports (2022) Vol. 147, pp. 100645-100645
Open Access | Times Cited: 91
Recent applications of machine learning in alloy design: A review
Mingwei Hu, Qiyang Tan, Ruth Knibbe, et al.
Materials Science and Engineering R Reports (2023) Vol. 155, pp. 100746-100746
Closed Access | Times Cited: 57
Frontiers in high entropy alloys and high entropy functional materials
Wentao Zhang, Xueqian Wang, Fengqi Zhang, et al.
Rare Metals (2024) Vol. 43, Iss. 10, pp. 4639-4776
Closed Access | Times Cited: 21
A Comprehensive Review on Hot Deformation Behavior of High-Entropy Alloys for High Temperature Applications
Reliance Jain, Sandeep Jain, Cheenepalli Nagarjuna, et al.
Metals and Materials International (2025)
Closed Access | Times Cited: 2
High‐Entropy Energy Materials in the Age of Big Data: A Critical Guide to Next‐Generation Synthesis and Applications
Qingsong Wang, Leonardo Velasco, Ben Breitung, et al.
Advanced Energy Materials (2021) Vol. 11, Iss. 47
Open Access | Times Cited: 76
Machine learning assisted modelling and design of solid solution hardened high entropy alloys
Xiaoya Huang, Cheng Jin, Chi Zhang, et al.
Materials & Design (2021) Vol. 211, pp. 110177-110177
Open Access | Times Cited: 69
Accelerated discovery of single-phase refractory high entropy alloys assisted by machine learning
Yonggang Yan, Dan Lü, Kun Wang
Computational Materials Science (2021) Vol. 199, pp. 110723-110723
Closed Access | Times Cited: 64
Predict the phase formation of high-entropy alloys by compositions
Qingwei Guo, Xiaotao Xu, Xiaolong Pei, et al.
Journal of Materials Research and Technology (2022) Vol. 22, pp. 3331-3339
Open Access | Times Cited: 61
Machine learning-enabled framework for the prediction of mechanical properties in new high entropy alloys
Amit Singh Bundela, M.R. Rahul
Journal of Alloys and Compounds (2022) Vol. 908, pp. 164578-164578
Closed Access | Times Cited: 49
A review of the multi-dimensional application of machine learning to improve the integrated intelligence of laser powder bed fusion
Kun Li, Ruijin Ma, Qin Yu, et al.
Journal of Materials Processing Technology (2023) Vol. 318, pp. 118032-118032
Closed Access | Times Cited: 41
Spinodal decomposition and the pseudo-binary decomposition in high-entropy alloys
Hengwei Luan, Liufei Huang, Jingyi Kang, et al.
Acta Materialia (2023) Vol. 248, pp. 118775-118775
Closed Access | Times Cited: 40
Phase prediction and experimental realisation of a new high entropy alloy using machine learning
Swati Singh, Nirmal Kumar Katiyar, Saurav Goel, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 38
Current application status of multi-scale simulation and machine learning in research on high-entropy alloys
Deyu Jiang, Lechun Xie, Liqiang Wang
Journal of Materials Research and Technology (2023) Vol. 26, pp. 1341-1374
Open Access | Times Cited: 36
High-entropy alloys for accessing hydrogen economy via sustainable production of fuels and direct application in fuel cells
Qiyang Cheng, Mengfan Wang, Jiajie Ni, et al.
Rare Metals (2023) Vol. 42, Iss. 11, pp. 3553-3569
Closed Access | Times Cited: 36
Accelerated design of high entropy alloys by integrating high throughput calculation and machine learning
Adarsh Bansal, Pankaj Kumar, Shubham Yadav, et al.
Journal of Alloys and Compounds (2023) Vol. 960, pp. 170543-170543
Closed Access | Times Cited: 34
Designing CoCrFeNi-M (M = Nb, Ta, Zr, and Hf) eutectic high-entropy alloys via a modified simple mixture method
Wenna Jiao, Junwei Miao, Yiping Lu, et al.
Journal of Alloys and Compounds (2023) Vol. 941, pp. 168975-168975
Closed Access | Times Cited: 32
Machine-learning-guided phase identification and hardness prediction of Al-Co-Cr-Fe-Mn-Nb-Ni-V containing high entropy alloys
Reliance Jain, Unhae Lee, Sumanta Samal, et al.
Journal of Alloys and Compounds (2023) Vol. 956, pp. 170193-170193
Closed Access | Times Cited: 32
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 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
Review on applications of artificial neural networks to develop high entropy alloys: A state-of-the-art technique
Sheetal Kumar Dewangan, Cheenepalli Nagarjuna, Reliance Jain, et al.
Materials Today Communications (2023) Vol. 37, pp. 107298-107298
Closed Access | Times Cited: 23
Machine Learning Design for High-Entropy Alloys: Models and Algorithms
S. Liu, Chao Yang
Metals (2024) Vol. 14, Iss. 2, pp. 235-235
Open Access | Times Cited: 15
High-throughput and data-driven machine learning techniques for discovering high-entropy alloys
Lu Zhichao, Dong Ma, Liu Xiongjun, et al.
Communications Materials (2024) Vol. 5, Iss. 1
Open Access | Times Cited: 13
Machine learning guided BCC or FCC phase prediction in high entropy alloys
Zhongping He, Huan Zhang, Hong Cheng, et al.
Journal of Materials Research and Technology (2024) Vol. 29, pp. 3477-3486
Open Access | Times Cited: 12
Enhancing flow stress predictions in CoCrFeNiV high entropy alloy with conventional and machine learning techniques
Sheetal Kumar Dewangan, Reliance Jain, Soumyabrata Bhattacharjee, et al.
Journal of Materials Research and Technology (2024) Vol. 30, pp. 2377-2387
Open Access | Times Cited: 10
Machine learning prediction and characterization of sigma-free high-entropy alloys
Mohammad Sajad Mehranpour, Ali Koushki, Seyed Soroush Karimi Madahi, et al.
Materials Characterization (2024) Vol. 212, pp. 113937-113937
Closed Access | Times Cited: 10
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