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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:

Machine learning assisted prediction of mechanical properties of graphene/aluminium nanocomposite based on molecular dynamics simulation
Jun Liu, Yingyan Zhang, Yihe Zhang, et al.
Materials & Design (2021) Vol. 213, pp. 110334-110334
Open Access | Times Cited: 75

Showing 1-25 of 75 citing articles:

Enhancing property prediction and process optimization in building materials through machine learning: A review
Konstantinos I. Stergiou, Charis Ntakolia, Paris Varytis, et al.
Computational Materials Science (2023) Vol. 220, pp. 112031-112031
Open Access | Times Cited: 89
EMCS-SVR: Hybrid efficient and accurate enhanced simulation approach coupled with adaptive SVR for structural reliability analysis
Changqi Luo, Behrooz Keshtegar, Shun‐Peng Zhu, et al.
Computer Methods in Applied Mechanics and Engineering (2022) Vol. 400, pp. 115499-115499
Closed Access | Times Cited: 70
Analysis of the friction and wear of graphene reinforced aluminum metal matrix composites using machine learning models
Md Syam Hasan, Tien Yin Wong, Pradeep K. Rohatgi, et al.
Tribology International (2022) Vol. 170, pp. 107527-107527
Closed Access | Times Cited: 69
Artificial Intelligence in Predicting Mechanical Properties of Composite Materials
Fasikaw Kibrete, Tomasz Trzepieciński, Hailu Shimels Gebremedhen, et al.
Journal of Composites Science (2023) Vol. 7, Iss. 9, pp. 364-364
Open Access | Times Cited: 63
Advances in machine learning-aided design of reinforced polymer composite and hybrid material systems
Christian Emeka Okafor, Sunday Iweriolor, Okwuchukwu Innocent Ani, et al.
Hybrid Advances (2023) Vol. 2, pp. 100026-100026
Open Access | Times Cited: 62
The effects of nano-additives on the mechanical, impact, vibration, and buckling/post-buckling properties of composites: A review
L. Shan, C.Y. Tan, Xing Shen, et al.
Journal of Materials Research and Technology (2023) Vol. 24, pp. 7570-7598
Open Access | Times Cited: 45
Optimization of mechanical properties of multiscale hybrid polymer nanocomposites: A combination of experimental and machine learning techniques
Elizabeth Champa-Bujaico, Ana M. Díez‐Pascual, Alba Lomas Redondo, et al.
Composites Part B Engineering (2023) Vol. 269, pp. 111099-111099
Open Access | Times Cited: 43
Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures
Vera Kuznetsova, Áine Coogan, Dmitry Botov, et al.
Advanced Materials (2024) Vol. 36, Iss. 18
Open Access | Times Cited: 22
Machine learning applications in nanomaterials: Recent advances and future perspectives
Liang Yang, Hong Wang, Deying Leng, et al.
Chemical Engineering Journal (2024), pp. 156687-156687
Closed Access | Times Cited: 17
Prediction of wear performance of ZK60 / CeO2 composites using machine learning models
Fatih Aydın, Rafet Durgut, Mustafa Mustu, et al.
Tribology International (2022) Vol. 177, pp. 107945-107945
Closed Access | Times Cited: 48
Application of DQHFEM for free and forced vibration, energy absorption, and post-buckling analysis of a hybrid nanocomposite viscoelastic rhombic plate assuming CNTs’ waviness and agglomeration
P.H. Wan, M.S.H. Al-Furjan, Reza Kolahchi, et al.
Mechanical Systems and Signal Processing (2023) Vol. 189, pp. 110064-110064
Closed Access | Times Cited: 35
Machine Learning-Driven Multidomain Nanomaterial Design: From Bibliometric Analysis to Applications
Hong Wang, Hengyu Cao, Liang Yang
ACS Applied Nano Materials (2024)
Closed Access | Times Cited: 9
Interfacial wetting characteristics of Na3AlF6-Al2O3-CaF2 slag with SiC: experiment and molecular dynamics simulation
Wandong Cheng, Zhiyuan Rui, Haobo Sun, et al.
Ceramics International (2025)
Closed Access | Times Cited: 1
A Generative Approach to Materials Discovery, Design, and Optimization
Dhruv Menon, Raghavan Ranganathan
ACS Omega (2022) Vol. 7, Iss. 30, pp. 25958-25973
Open Access | Times Cited: 36
Predicting hydrogen storage capacity of V–Ti–Cr–Fe alloy via ensemble machine learning
Ziliang Lu, Jianwei Wang, Yuanfang Wu, et al.
International Journal of Hydrogen Energy (2022) Vol. 47, Iss. 81, pp. 34583-34593
Closed Access | Times Cited: 29
Comparison of traditional and automated machine learning approaches in predicting the compressive strength of graphene oxide/cement composites
Jinlong Yang, Bowen Zeng, Ni Zhi, et al.
Construction and Building Materials (2023) Vol. 394, pp. 132179-132179
Closed Access | Times Cited: 21
Machine learning modeling for the prediction of plastic properties in metallic glasses
Nicolás Amigó, Simón Palominos, Felipe J. Valencia
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 19
Maximizing Triboelectric Nanogenerators by Physics‐Informed AI Inverse Design
Pengcheng Jiao, Zhong Lin Wang, Amir H. Alavi
Advanced Materials (2023) Vol. 36, Iss. 5
Open Access | Times Cited: 19
Aeroelastic analyses of functionally graded aluminium composite plates reinforced with graphene nanoplatelets under fluid-structural interaction
Jun Liu, Yingyan Zhang, Yihe Zhang, et al.
Aerospace Science and Technology (2023) Vol. 136, pp. 108254-108254
Closed Access | Times Cited: 17
Primary irradiation damage in Ni–graphene nanocomposites with pre-existing hydrogen: insights from atomistic simulations
Tonghe Liu, Xiaoting Yuan, Hai Huang
The European Physical Journal Plus (2024) Vol. 139, Iss. 1
Closed Access | Times Cited: 6
Multiscale computational modeling techniques in study and design of 2D materials: recent advances, challenges, and opportunities
Mohsen Asle Zaeem, Siby Thomas, Sepideh Kavousi, et al.
2D Materials (2024) Vol. 11, Iss. 4, pp. 042004-042004
Open Access | Times Cited: 6
Prediction of Mechanical Properties of 3D Printed Particle-Reinforced Resin Composites
Kimberley Rooney, Yu Dong, A.K. Basak, et al.
Journal of Composites Science (2024) Vol. 8, Iss. 10, pp. 416-416
Open Access | Times Cited: 6
Machine Learning Approaches for Predicting the Ablation Performance of Ceramic Matrix Composites
Jayanta Bhusan Deb, Jihua Gou, Haonan Song, et al.
Journal of Composites Science (2024) Vol. 8, Iss. 3, pp. 96-96
Open Access | Times Cited: 5
Aluminium-graphene metal matrix nanocomposites: Modelling, analysis, and simulation approach to estimate mechanical properties
Mamta Dahiya, Virat Khanna, Suneev Anil Bansal
Materials Today Proceedings (2022) Vol. 78, pp. 414-419
Closed Access | Times Cited: 25
Triboinformatics: machine learning algorithms and data topology methods for tribology
Md Syam Hasan, Michael Nosonovsky
Surface Innovations (2022) Vol. 10, Iss. 4-5, pp. 229-242
Closed Access | Times Cited: 23
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