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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:

Modeling glass-forming ability of bulk metallic glasses using computational intelligent techniques
Abdul Majid, Syed Bilal Ahsan, Naeem ul Haq Tariq
Applied Soft Computing (2014) Vol. 28, pp. 569-578
Closed Access | Times Cited: 21

Showing 21 citing articles:

A new correlation between the characteristics temperature and glass-forming ability for bulk metallic glasses
Zhilin Long, Wei Liu, Ming Zhong, et al.
Journal of Thermal Analysis and Calorimetry (2018) Vol. 132, Iss. 3, pp. 1645-1660
Closed Access | Times Cited: 60
Key feature space for predicting the glass-forming ability of amorphous alloys revealed by gradient boosted decision trees model
X.W. Liu, Zhilin Long, W. Zhang, et al.
Journal of Alloys and Compounds (2022) Vol. 901, pp. 163606-163606
Closed Access | Times Cited: 31
Discovery of novel quaternary bulk metallic glasses using a developed correlation-based neural network approach
Majid Samavatian, Reza Gholamipour, Vahid Samavatian
Computational Materials Science (2020) Vol. 186, pp. 110025-110025
Closed Access | Times Cited: 45
Design of Fe-based bulk metallic glasses for maximum amorphous diameter (Dmax) using machine learning models
Daniel Mastropietro, J. Moya
Computational Materials Science (2020) Vol. 188, pp. 110230-110230
Closed Access | Times Cited: 34
A two-step fused machine learning approach for the prediction of glass-forming ability of metallic glasses
Yivven Zhang, Guichuan Xing, Zhen-Dong Sha, et al.
Journal of Alloys and Compounds (2021) Vol. 875, pp. 160040-160040
Closed Access | Times Cited: 29
Recent development of chemically complex metallic glasses: from accelerated compositional design, additive manufacturing to novel applications
Jingyang Zhang, Zi-Qi Zhou, Zhibing Zhang, et al.
Materials Futures (2021) Vol. 1, Iss. 1, pp. 012001-012001
Open Access | Times Cited: 29
Structural, prediction and simulation of elastic properties for tellurite based glass systems doped with nano and micro Eu2O3 particles via artificial neural network model
Suleiman Bashir Adamu, M.K. Halimah, Kevin T. Chan, et al.
Journal of Materials Research and Technology (2022) Vol. 17, pp. 586-600
Open Access | Times Cited: 20
Prediction of glass forming ability of bulk metallic glasses based on convolutional neural network
Ting Zhang, Zhilin Long, Li Peng, et al.
Journal of Non-Crystalline Solids (2022) Vol. 595, pp. 121846-121846
Closed Access | Times Cited: 18
Discovery of a new criterion for predicting glass-forming ability based on symbolic regression and artificial neural network
Baofeng Tan, Yongchao Liang, Qian Chen, et al.
Journal of Applied Physics (2022) Vol. 132, Iss. 12
Closed Access | Times Cited: 15
Machine Learning Aided Prediction of Glass-Forming Ability of Metallic Glass
Chengcheng Liu, Xuandong Wang, Weidong Cai, et al.
Processes (2023) Vol. 11, Iss. 9, pp. 2806-2806
Open Access | Times Cited: 7
Glass forming ability prediction of bulk metallic glasses based on fused strategy
Ting Zhang, Zhilin Long, Li PENG
Transactions of Nonferrous Metals Society of China (2024) Vol. 34, Iss. 5, pp. 1558-1570
Open Access | Times Cited: 2
Inverse Design of Fe-Based Bulk Metallic Glasses Using Machine Learning
Junhyub Jeon, Namhyuk Seo, Hwi-Jun Kim, et al.
Metals (2021) Vol. 11, Iss. 5, pp. 729-729
Open Access | Times Cited: 16
A critical review of the machine learning guided design of metallic glasses for superior glass-forming ability
Ziqing Zhou, Yinghui Shang, Yong Yang
Journal of Materials Informatics (2022)
Open Access | Times Cited: 9
A multi-strategy hybrid machine learning model for predicting glass-formation ability of metallic glasses based on imbalanced datasets
Xiaowei Liu, Zhilin Long, Wei Zhang, et al.
Journal of Non-Crystalline Solids (2023) Vol. 621, pp. 122645-122645
Closed Access | Times Cited: 4
Explicit expressions of the saturation flux density and thermal stability in Fe-based metallic glasses based on Lasso regression
Zhuang Li, Zhilin Long, Shan Lei, et al.
Intermetallics (2021) Vol. 139, pp. 107361-107361
Closed Access | Times Cited: 10
Prediction of Glass Forming Ability of Bulk Metallic Glasses Using Machine Learning
G. Jaideep Reddy, Manjunadh Kandavalli, Tanay Saboo, et al.
Integrating materials and manufacturing innovation (2021) Vol. 10, Iss. 4, pp. 610-626
Closed Access | Times Cited: 9
Optimization of convolutional neural network for glass-forming ability prediction based on particle swarm optimization
Meng-qi Wang, Yongchao Liang, Bo Sun, et al.
Materials Today Communications (2023) Vol. 36, pp. 106808-106808
Closed Access | Times Cited: 3
Developing a multilateral-based neural network model for engineering of high entropy amorphous alloys
Tzu-Chia Chen, Marischa Elveny, A. Surendar, et al.
Modelling and Simulation in Materials Science and Engineering (2021) Vol. 29, Iss. 6, pp. 065019-065019
Closed Access | Times Cited: 8
Does the GFA of Alloys Depend on the Atomic Size Ratio: A DT-Based ML Study
Jiaming Pan, Yushuang Jia, Kenli Li, et al.
Crystal Growth & Design (2023) Vol. 23, Iss. 8, pp. 5912-5918
Closed Access | Times Cited: 2
Ensemble learning predicts glass-forming ability under imbalanced datasets
Dehua Cheng, Yongchao Liang, Yuanwei Pu, et al.
Computational Materials Science (2024) Vol. 248, pp. 113601-113601
Closed Access
Prediction of Glass Forming Ability of Bulk Metallic Glasses-Machine-Learning
Jaideep Reddy Gedi, Tanay Saboo, A.K. Prasada Rao
Research Square (Research Square) (2021)
Open Access
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