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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:

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: 90

Showing 1-25 of 90 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: 288
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
Scope of machine learning in materials research—A review
Md Hosne Mobarak, Mariam Akter Mimona, Md Aminul Islam, et al.
Applied Surface Science Advances (2023) Vol. 18, pp. 100523-100523
Open Access | Times Cited: 65
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: 45
A review on copper alloys with high strength and high electrical conductivity
Qingzhong Mao, Yanfang Liu, Yonghao Zhao
Journal of Alloys and Compounds (2024) Vol. 990, pp. 174456-174456
Closed Access | Times Cited: 45
Ultrafine-grained Mg alloy: Preparation, properties, design strategy
Peng Peng, Hansong Xue, Jia She, et al.
Journal of Materials Research and Technology (2024) Vol. 29, pp. 4480-4504
Open Access | Times Cited: 19
Recent advances and outstanding challenges for implementation of high entropy alloys as structural materials
Mikhail Slobodyan, Evgeniy Pesterev, A. B. Markov
Materials Today Communications (2023) Vol. 36, pp. 106422-106422
Closed Access | Times Cited: 41
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: 38
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: 27
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‐based approach for fatigue crack growth prediction using acoustic emission technique
Mengyu Chai, Pan Liu, Yuhang He, et al.
Fatigue & Fracture of Engineering Materials & Structures (2023) Vol. 46, Iss. 8, pp. 2784-2797
Closed Access | Times Cited: 17
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: 8
Research on hot deformation behavior of Cu-Ti alloy based on machine learning algorithms and microalloying
Mengxiao Zhang, Dayong Chen, Huan Liu, et al.
Materials Today Communications (2024) Vol. 39, pp. 108783-108783
Closed Access | Times Cited: 6
Investigation of the Corrosion Inhibitory Effectiveness of Castor Leaf Extract as a Corrosion Inhibitor for Steel in a Sulfuric Acid Environment
Hongda Deng, Бо Ли, Haiqin Ren, et al.
Journal of environmental chemical engineering (2024) Vol. 12, Iss. 5, pp. 113974-113974
Closed Access | Times Cited: 6
Machine learning guided optimal composition selection of niobium alloys for high temperature applications
Trupti Mohanty, K.S. Ravi Chandran, Taylor D. Sparks
APL Machine Learning (2023) Vol. 1, Iss. 3
Open Access | Times Cited: 15
Machine learning approach for predicting electrical features of Schottky structures with graphene and ZnTiO3 nanostructures doped in PVP interfacial layer
Ali Barkhordari, Hamid Reza Mashayekhi, Pari Amiri, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 15
Accelerating the Discovery of Transition Metal Borides by Machine Learning on Small Data Sets
Yuqi Sun, Guanjie Wang, Kaiqi Li, et al.
ACS Applied Materials & Interfaces (2023) Vol. 15, Iss. 24, pp. 29278-29286
Closed Access | Times Cited: 14
Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature
Mengwei Wu, Wei Yong, Cunqin Fu, et al.
International Journal of Minerals Metallurgy and Materials (2024) Vol. 31, Iss. 4, pp. 773-785
Closed Access | Times Cited: 5
A brief review of machine learning-assisted Mg alloy design, processing, and property predictions
Yanhui Cheng, Lifei Wang, Chaoyang Yang, et al.
Journal of Materials Research and Technology (2024) Vol. 30, pp. 8108-8127
Open Access | Times Cited: 5
Accelerated discovery of high-performance Al-Si-Mg-Sc casting alloys by integrating active learning with high-throughput CALPHAD calculations
Jianbao Gao, Jing Zhong, Guangchen Liu, et al.
Science and Technology of Advanced Materials (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 12
Prediction of ideal strength by machine learning
Zhao Liu, Biao Wang
Materials Chemistry and Physics (2023) Vol. 299, pp. 127476-127476
Closed Access | Times Cited: 11
Microstructure characterization and tensile performance of a high-strength titanium alloy with in-situ precipitates of Ti5Si3
Longchao Zhuo, Kaile Ji, Jinwen Lu, et al.
Journal of Alloys and Compounds (2023) Vol. 968, pp. 171867-171867
Closed Access | Times Cited: 11
Predicting the in-plane mechanical anisotropy of 7085 aluminum alloys through crystal plasticity simulations and machine learning
Zhichen Zhang, Zuosheng Li, Sai Tang, et al.
Materials Today Communications (2024) Vol. 38, pp. 108381-108381
Closed Access | Times Cited: 4
Enhancing Mechanical Behavior Assessment in Porous Thermal Barrier Coatings using a Machine Learning Fine-Tuned with Genetic Algorithm
Ahmed A. H. Alkurdi, Hani K. Al-Mohair, Paul Rodrigues, et al.
Journal of Thermal Spray Technology (2024) Vol. 33, Iss. 4, pp. 824-838
Closed Access | Times Cited: 4
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