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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:

High-throughput map design of creep life in low-alloy steels by integrating machine learning with a genetic algorithm
Chenchong Wang, Xiaolu Wei, Da Ren, et al.
Materials & Design (2021) Vol. 213, pp. 110326-110326
Open Access | Times Cited: 37

Showing 1-25 of 37 citing articles:

Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction
Jin Li, Naiteng Wu, Jian Zhang, et al.
Nano-Micro Letters (2023) Vol. 15, Iss. 1
Open Access | Times Cited: 66
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
Review on Cellular Automata for Microstructure Simulation of Metallic Materials
Ying Zhi, Yao Jiang, Diwen Ke, et al.
Materials (2024) Vol. 17, Iss. 6, pp. 1370-1370
Open Access | Times Cited: 7
Customized development of promising Cu-Cr-Ni-Co-Si alloys enabled by integrated machine learning and characterization
Shaobin Pan, Jinxin Yu, Jiajia Han, et al.
Acta Materialia (2022) Vol. 243, pp. 118484-118484
Closed Access | Times Cited: 25
An intelligent design for Ni-based superalloy based on machine learning and multi-objective optimization
Yuedan Deng, Yu Zhang, Xiufang Gong, et al.
Materials & Design (2022) Vol. 221, pp. 110935-110935
Open Access | Times Cited: 24
Research on multi-source microstructure image recognition of foam ceramics using convolutional network combine with frequency domain
Yi Yin, Jianwei Pan, Fang Wang, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Stochastic Room Temperature Creep of 316l Stainless Steel
Samuel Inman, Kevin Wayne Garber, Aaron A. Robertson, et al.
(2025)
Closed Access
Predicting creep failure life in adhesive-bonded single-lap joints using machine learning
Faizullah Jan, Marcin Kujawa, Piotr Paczos, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
High-temperature compression behavior prediction of medium Mn steel: a comparative study of Arrhenius constitutive equation, machine learning, and symbolic regression models
Boyuan Huang, Z. Y. Zhang, Shuai Zhao, et al.
Journal of Materials Science (2025)
Closed Access
Prediction of Creep Rupture Life of 5Cr-0.5Mo Steel Using Machine Learning Models
Muhammad Ishtiaq, Hafiz Muhammad Rehan Tariq, D. Krishna Reddy, et al.
Metals (2025) Vol. 15, Iss. 3, pp. 288-288
Open Access
Stochastic Room Temperature Creep of 316L Stainless Steel
Samuel B. Inman, Kevin Wayne Garber, Andreas E. Robertson, et al.
International Journal of Plasticity (2025), pp. 104326-104326
Closed Access
High-throughput design strategy for creep-resistance steel using machine learning and optimization algorithm
Ci‐Ling Pan, Chenchong Wang, Yuqi Zhang, et al.
Materials Today Communications (2025) Vol. 46, pp. 112467-112467
Closed Access
Strength Investigation and Prediction of Superfine Tailings Cemented Paste Backfill Based on Experiments and Intelligent Methods
Yafei Hu, Keqing Li, Bo Zhang, et al.
Materials (2023) Vol. 16, Iss. 11, pp. 3995-3995
Open Access | Times Cited: 11
Alloys innovation through machine learning: a statistical literature review
Alireza Valizadeh, Ryoji Sahara, Maaouia Souissi
Science and Technology of Advanced Materials Methods (2024) Vol. 4, Iss. 1
Open 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
Machine Learning in Biomaterials, Biomechanics/Mechanobiology, and Biofabrication: State of the Art and Perspective
Chi Wu, Yanan Xu, Jianguang Fang, et al.
Archives of Computational Methods in Engineering (2024)
Open Access | Times Cited: 4
Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations
Desmond Klenam, T.K. Asumadu, Mobin Vandadi, et al.
Results in Materials (2023) Vol. 19, pp. 100455-100455
Open Access | Times Cited: 9
Composition, heat treatment, microstructure and loading condition based machine learning prediction of creep life of superalloys
Ronghai Wu, Zeng Lei, Jiangkun Fan, et al.
Mechanics of Materials (2023) Vol. 187, pp. 104819-104819
Closed Access | Times Cited: 9
Machine learning-based performance predictions for steels considering manufacturing process parameters: a review
Wei Fang, Jiaxin Huang, Tiexu Peng, et al.
Journal of Iron and Steel Research International (2024) Vol. 31, Iss. 7, pp. 1555-1581
Closed Access | Times Cited: 3
Machine learning and Python assisted design and verification of Fe–based amorphous/nanocrystalline alloy
Yichuan Tang, Yuan Wan, Zhongqi Wang, et al.
Materials & Design (2022) Vol. 219, pp. 110726-110726
Open Access | Times Cited: 15
Recent Advances on Composition-Microstructure-Properties Relationships of Precipitation Hardening Stainless Steel
Puchang Cui, Geshu Xing, Zhisheng Nong, et al.
Materials (2022) Vol. 15, Iss. 23, pp. 8443-8443
Open Access | Times Cited: 13
Machine Learning-Based Framework for Predicting Creep Rupture Life of Modified 9Cr-1Mo Steel
Mengyu Chai, Yuhang He, Yongquan Li, et al.
Applied Sciences (2023) Vol. 13, Iss. 8, pp. 4972-4972
Open Access | Times Cited: 7
A Method for Predicting the Creep Rupture Life of Small-Sample Materials Based on Parametric Models and Machine Learning Models
Xu Zhang, Jianyao Yao, Yulin Wu, et al.
Materials (2023) Vol. 16, Iss. 20, pp. 6804-6804
Open Access | Times Cited: 7
Creep rupture life predictions for Ni-based single crystal superalloys with automated machine learning
Changlu Zhou, Ruihao Yuan, Weijie Liao, et al.
Rare Metals (2024) Vol. 43, Iss. 6, pp. 2884-2890
Closed Access | Times Cited: 2
Simple Data Analytics Approach Coupled with Larson–Miller Parameter Analysis for Improved Prediction of Creep Rupture Life
Chang-Ho Lee, Taejoo Lee, Yoon Suk Choi
Metals and Materials International (2023) Vol. 29, Iss. 11, pp. 3149-3160
Closed Access | Times Cited: 5
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