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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:

Prediction of phase and hardness of HEAs based on constituent elements using machine learning models
Mahmoud Bakr, Junaidi Syarif, Mohamed Hashem
Materials Today Communications (2022) Vol. 31, pp. 103407-103407
Closed Access | Times Cited: 26

Showing 1-25 of 26 citing articles:

Machine learning accelerates the materials discovery
Jiheng Fang, Ming Xie, Xingqun He, et al.
Materials Today Communications (2022) Vol. 33, pp. 104900-104900
Closed Access | Times Cited: 82
Bio-high entropy alloys: Progress, challenges, and opportunities
Junyi Feng, Yujin Tang, Jia Liu, et al.
Frontiers in Bioengineering and Biotechnology (2022) Vol. 10
Open Access | Times Cited: 50
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: 39
Predicting the hardness of high-entropy alloys based on compositions
Qingwei Guo, Yue Pan, Hua Hou, et al.
International Journal of Refractory Metals and Hard Materials (2023) Vol. 112, pp. 106116-106116
Closed Access | Times Cited: 35
Machine Learning Paves the Way for High Entropy Compounds Exploration: Challenges, Progress, and Outlook
Xuhao Wan, Zeyuan Li, Wei Yu, et al.
Advanced Materials (2023)
Closed Access | Times Cited: 31
Molecular dynamics simulation and machine learning-based analysis for predicting tensile properties of high-entropy FeNiCrCoCu alloys
Omarelfarouq Elgack, Belal Almomani, Junaidi Syarif, et al.
Journal of Materials Research and Technology (2023) Vol. 25, pp. 5575-5585
Open Access | Times Cited: 29
Improving the performance of machine learning model predicting phase and crystal structure of high entropy alloys by the synthetic minority oversampling technique
K Hareharen, T. Panneerselvam, R. Raj Mohan
Journal of Alloys and Compounds (2024) Vol. 991, pp. 174494-174494
Closed Access | Times Cited: 9
Prediction and design of high hardness high entropy alloy through machine learning
Wei Ren, Yifan Zhang, Weili Wang, et al.
Materials & Design (2023) Vol. 235, pp. 112454-112454
Open Access | Times Cited: 22
Machine learning prediction of hardness in solid solution high entropy alloys
Zhiyu Gao, Fei Zhao, Sida Gao, et al.
Materials Today Communications (2023) Vol. 37, pp. 107102-107102
Closed Access | Times Cited: 17
Machine Learning-Based Hardness Prediction of High-Entropy Alloys for Laser Additive Manufacturing
Wenhan Zhu, Wenyi Huo, Shiqi Wang, et al.
JOM (2023) Vol. 75, Iss. 12, pp. 5537-5548
Open Access | Times Cited: 15
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
AlloyManufacturingNet for discovery and design of hardness-elongation synergy in multi-principal element alloys
Sachin Poudel, Upadesh Subedi, Mohammed O.A. Hamid, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 132, pp. 107902-107902
Closed Access | Times Cited: 4
Prediction of electrochemical impedance spectroscopy of high-entropy alloys corrosion by using gradient boosting decision tree
Boxin Wei, Jin Xu, Jingyu Pang, et al.
Materials Today Communications (2022) Vol. 32, pp. 104047-104047
Closed Access | Times Cited: 17
Deep Learning-Based Framework for Efficient Design of Multicomponent High Hardness High Entropy Alloys
Yuexing Han, Hui Wang, Pengfei Xu, et al.
ACS Applied Materials & Interfaces (2025)
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
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
Hardness prediction of WC-Co cemented carbide based on machine learning model
Rui Song, Xuemei Liu, Haibin Wang, et al.
Acta Physica Sinica (2024) Vol. 73, Iss. 12, pp. 126201-126201
Open Access | Times Cited: 2
Machine learning-aided phase and mechanical properties prediction in multi-principal element alloys
Ehsan Gerashi, Mahdi Pourbaghi, Xili Duan, et al.
Computational Materials Science (2024) Vol. 243, pp. 113114-113114
Closed Access | Times Cited: 2
Stacking ensemble learning assisted design of Al-Nb-Ti-V-Zr lightweight high-entropy alloys with high hardness
Q.Z. Chen, Zijian He, Yi Zhao, et al.
Materials & Design (2024), pp. 113363-113363
Open Access | Times Cited: 2
A machine learning framework for discovering high entropy alloys phase formation drivers
Junaidi Syarif, Mahmoud B. Elbeltagy, Ali Bou Nassif
Heliyon (2023) Vol. 9, Iss. 1, pp. e12859-e12859
Open Access | Times Cited: 6
Machine learning combined with solid solution strengthening model for predicting hardness of high entropy alloys
Yifan Zhang, Wei Ren, Weili Wang, et al.
Acta Physica Sinica (2023) Vol. 72, Iss. 18, pp. 180701-180701
Open Access | Times Cited: 4
Machine learning based phase prediction and powder metallurgy assisted experimental validation of medium entropy compositionally complex alloys
P. Das, Pulak M. Pandey
Modelling and Simulation in Materials Science and Engineering (2023) Vol. 31, Iss. 8, pp. 085015-085015
Closed Access | Times Cited: 4
Smart manufacturing with transfer learning under limited data: Towards Data-Driven Intelligences
Abid Hasan Zim, Aquib Iqbal, Liakat Hossain, et al.
Materials Today Communications (2023) Vol. 37, pp. 107357-107357
Closed Access | Times Cited: 2
MODELING THE HARDENING OF CARBON STEELS AFTER QUENCHING AND TEMPERING
Nikolay Tontchev, Normunds Teirumnieks, Emil Yankov
Environment Technology Resources Proceedings of the International Scientific and Practical Conference (2024) Vol. 3, pp. 456-459
Open Access
Supervised machine learning for multi-principal element alloy structural design
J. Berry, Katerina A. Christofidou
Materials Science and Technology (2024)
Open Access
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