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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:

A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness
Yang Chen, Chang Ren, Yuefei Jia, et al.
Acta Materialia (2021) Vol. 222, pp. 117431-117431
Closed Access | Times Cited: 183

Showing 1-25 of 183 citing articles:

Small data machine learning in materials science
Pengcheng Xu, Xiaobo Ji, Minjie Li, et al.
npj Computational Materials (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 248
Machine learning for high-entropy alloys: Progress, challenges and opportunities
Xianglin Liu, Jiaxin Zhang, Zongrui Pei
Progress in Materials Science (2022) Vol. 131, pp. 101018-101018
Open Access | Times Cited: 170
Explainable machine learning in materials science
Xiaoting Zhong, Brian Gallagher, Shusen Liu, et al.
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 140
Material machine learning for alloys: Applications, challenges and perspectives
Xiujuan Liu, Pengcheng Xu, Juanjuan Zhao, et al.
Journal of Alloys and Compounds (2022) Vol. 921, pp. 165984-165984
Closed Access | Times Cited: 82
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: 71
Interpretable hardness prediction of high-entropy alloys through ensemble learning
Yifan Zhang, Wei Ren, Weili Wang, et al.
Journal of Alloys and Compounds (2023) Vol. 945, pp. 169329-169329
Closed Access | Times Cited: 40
A strong-yet-ductile high-entropy alloy in a broad temperature range from cryogenic to elevated temperatures
Yinghao Zhou, Jinyong Zhang, Jingyang Zhang, et al.
Acta Materialia (2024) Vol. 268, pp. 119770-119770
Closed Access | Times Cited: 24
Machine learning assisted design of high-entropy alloys with ultra-high microhardness and unexpected low density
Shunli Zhao, Bin Jiang, Kaikai Song, et al.
Materials & Design (2024) Vol. 238, pp. 112634-112634
Open Access | Times Cited: 15
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: 14
MLMD: a programming-free AI platform to predict and design materials
Jiaxuan Ma, Bin Cao, Shuya Dong, et al.
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 13
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: 44
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: 35
A Critical Review of Machine Learning Techniques on Thermoelectric Materials
Xiangdong Wang, Ye Sheng, Jinyan Ning, et al.
The Journal of Physical Chemistry Letters (2023) Vol. 14, Iss. 7, pp. 1808-1822
Closed Access | Times Cited: 30
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: 29
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: 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: 25
Bayesian machine learning-aided approach bridges between dynamic elasticity and compressive strength in the cement-based mortars
Ning Wang, Majid Samavatian, Vahid Samavatian, et al.
Materials Today Communications (2023) Vol. 35, pp. 106283-106283
Closed Access | Times Cited: 25
Intelligent prediction model of mechanical properties of ultrathin niobium strips based on XGBoost ensemble learning algorithm
Zhenhua Wang, Yunfei Liu, Tao Wang, et al.
Computational Materials Science (2023) Vol. 231, pp. 112579-112579
Closed Access | Times Cited: 25
Feature Selection in Machine Learning for Perovskite Materials Design and Discovery
Junya Wang, Pengcheng Xu, Xiaobo Ji, et al.
Materials (2023) Vol. 16, Iss. 8, pp. 3134-3134
Open Access | Times Cited: 22
Machine learning accelerated discovery of corrosion-resistant high-entropy alloys
Cheng Zeng, Andrew Neils, Jack Lesko, et al.
Computational Materials Science (2024) Vol. 237, pp. 112925-112925
Open Access | Times Cited: 13
Designing of high entropy alloys with high hardness: a metaheuristic approach
Ansh Poonia, Modalavalasa Kishor, A.K. Prasada Rao
Scientific Reports (2024) Vol. 14, Iss. 1
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: 9
Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins
Yuanhao Xu, Kairong Lin, Caihong Hu, et al.
Journal of Hydrology (2024) Vol. 639, pp. 131598-131598
Closed Access | Times Cited: 9
Experimentally validated inverse design of multi-property Fe-Co-Ni alloys
Shakti P. Padhy, Varun Chaudhary, Yee-Fun Lim, et al.
iScience (2024) Vol. 27, Iss. 5, pp. 109723-109723
Open Access | Times Cited: 7
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: 31
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