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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:

Accelerating materials discovery using machine learning
Yongfei Juan, Yongbing Dai, Yang Yang, et al.
Journal of Material Science and Technology (2020) Vol. 79, pp. 178-190
Closed Access | Times Cited: 148

Showing 1-25 of 148 citing articles:

Machine learning in predicting mechanical behavior of additively manufactured parts
Sara Nasiri, Mohammad Reza Khosravani
Journal of Materials Research and Technology (2021) Vol. 14, pp. 1137-1153
Open Access | Times Cited: 136
Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects
Yiming Liu, Xinyu Tan, Jie Liang, et al.
Advanced Functional Materials (2023) Vol. 33, Iss. 17
Closed Access | Times Cited: 80
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: 78
New Opportunity: Machine Learning for Polymer Materials Design and Discovery
Pengcheng Xu, Huimin Chen, Minjie Li, et al.
Advanced Theory and Simulations (2022) Vol. 5, Iss. 5
Closed Access | Times Cited: 77
Machine learning in energy storage materials
Zhonghui Shen, Hanxing Liu, Yang Shen, et al.
Interdisciplinary materials (2022) Vol. 1, Iss. 2, pp. 175-195
Open Access | Times Cited: 70
A brief overview of solar and wind-based green hydrogen production systems: Trends and standardization
Münür Sacit Herdem, Domenico Mazzeo, Nicoletta Matera, et al.
International Journal of Hydrogen Energy (2023) Vol. 51, pp. 340-353
Closed Access | Times Cited: 64
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: 61
AI for Nanomaterials Development in Clean Energy and Carbon Capture, Utilization and Storage (CCUS)
Honghao Chen, Yingzhe Zheng, Jiali Li, et al.
ACS Nano (2023) Vol. 17, Iss. 11, pp. 9763-9792
Closed Access | Times Cited: 45
A comprehensive review of covalent organic frameworks (COFs) and their derivatives in environmental pollution control
Shengbo Ge, Kexin Wei, Wanxi Peng, et al.
Chemical Society Reviews (2024)
Open Access | Times Cited: 22
Combining machine learning and metal–organic frameworks research: Novel modeling, performance prediction, and materials discovery
Chunhua Li, Luqian Bao, Yixin Ji, et al.
Coordination Chemistry Reviews (2024) Vol. 514, pp. 215888-215888
Closed Access | Times Cited: 18
Predicting the crack repair rate of self-healing concrete using soft-computing tools
Yuanfeng Lou, Huiling Wang, Muhammad Nasir Amin, et al.
Materials Today Communications (2024) Vol. 38, pp. 108043-108043
Closed Access | Times Cited: 16
Machine Learning-Assisted High-Donor-Number Electrolyte Additive Screening toward Construction of Dendrite-Free Aqueous Zinc-Ion Batteries
Haoran Luo, Qianzhi Gou, Yu Zheng, et al.
ACS Nano (2025)
Closed Access | Times Cited: 4
Accelerating materials property prediction via a hybrid Transformer Graph framework that leverages four body interactions
Mohammad Madani, Valentina Lacivita, Yongwoo Shin, et al.
npj Computational Materials (2025) Vol. 11, Iss. 1
Open Access | Times Cited: 3
Artificial intelligence: machine learning for chemical sciences
A. Karthikeyan, U. Deva Priyakumar
Journal of Chemical Sciences (2021) Vol. 134, Iss. 1
Open Access | Times Cited: 66
A strategy assisted machine learning to process multi-objective optimization for improving mechanical properties of carbon steels
Yupeng Diao, Luchun Yan, Kewei Gao
Journal of Material Science and Technology (2021) Vol. 109, pp. 86-93
Closed Access | Times Cited: 63
Machine learning-guided property prediction of energetic materials: Recent advances, challenges, and perspectives
Xiaolan Tian, Siwei Song, Fang Chen, et al.
Energetic Materials Frontiers (2022) Vol. 3, Iss. 3, pp. 177-186
Open Access | Times Cited: 50
Recent developments in the application of machine-learning towards accelerated predictive multiscale design and additive manufacturing
Sandeep Suresh Babu, Abdel‐Hamid I. Mourad, Khalifa H. Harib, et al.
Virtual and Physical Prototyping (2022) Vol. 18, Iss. 1
Open Access | Times Cited: 47
Prediction of nature of band gap of perovskite oxides (ABO3) using a machine learning approach
G. Sudha Priyanga, Manoj N. Mattur, N. Nagappan, et al.
Journal of Materiomics (2022) Vol. 8, Iss. 5, pp. 937-948
Open Access | Times Cited: 46
Industrial big data-driven mechanical performance prediction for hot-rolling steel using lower upper bound estimation method
Gongzhuang Peng, Yinliang Cheng, Yufei Zhang, et al.
Journal of Manufacturing Systems (2022) Vol. 65, pp. 104-114
Closed Access | Times Cited: 40
X-ray Diffraction Data Analysis by Machine Learning Methods—A Review
Vasile-Adrian Surdu, Romuald Győrgy
Applied Sciences (2023) Vol. 13, Iss. 17, pp. 9992-9992
Open Access | Times Cited: 33
TRACING THE EVOLUTION OF AI AND MACHINE LEARNING APPLICATIONS IN ADVANCING MATERIALS DISCOVERY AND PRODUCTION PROCESSES
Nwakamma Ninduwezuor-Ehiobu, Olawe Alaba Tula, Chibuike Daraojimba, et al.
Engineering Science & Technology Journal (2023) Vol. 4, Iss. 3, pp. 66-83
Open Access | Times Cited: 33
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
The Application of Physics-Informed Machine Learning in Multiphysics Modeling in Chemical Engineering
Zhi‐Yong Wu, Huan Wang, Chang He, et al.
Industrial & Engineering Chemistry Research (2023) Vol. 62, Iss. 44, pp. 18178-18204
Closed Access | Times Cited: 25
Which molecular properties determine the impact sensitivity of an explosive? A machine learning quantitative investigation of nitroaromatic explosives
Júlio César Duarte, Romulo Dias da Rocha, Itamar Borges
Physical Chemistry Chemical Physics (2023) Vol. 25, Iss. 9, pp. 6877-6890
Open Access | Times Cited: 24
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
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