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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:

Prediction of Vickers hardness of amorphous alloys based on interpretable machine learning
Xiaowei Liu, Zhilin Long, Peng Li
Journal of Non-Crystalline Solids (2022) Vol. 602, pp. 122095-122095
Closed Access | Times Cited: 24

Showing 24 citing articles:

Advancing 3D bioprinting through machine learning and artificial intelligence
Srikanthan Ramesh, Akash Deep, Ali Tamayol, et al.
Bioprinting (2024) Vol. 38, pp. e00331-e00331
Closed Access | Times Cited: 28
Predicting the cumulative productivity of a solar distillation system augmented with a tilted absorber panel using machine learning models
Wissam H. Alawee, Luttfi A. Al-Haddad, Hayder A. Dhahad, et al.
Journal of Engineering Research (2024)
Open Access | Times Cited: 25
Efficient multidisciplinary modeling of aircraft undercarriage landing gear using data-driven Naïve Bayes and finite element analysis
Luttfi A. Al-Haddad, Nibras M. Mahdi
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 4, pp. 3187-3199
Closed Access | Times Cited: 17
Forecasting the productivity of a solar distiller enhanced with an inclined absorber plate using stochastic gradient descent in artificial neural networks
Suha A. Mohammed, Luttfi A. Al-Haddad, Wissam H. Alawee, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2023) Vol. 7, Iss. 3, pp. 1819-1829
Closed Access | Times Cited: 22
Forecasting sustainable water production in convex tubular solar stills using gradient boosting analysis
Wissam H. Alawee, Luttfi A. Al-Haddad, Ali Basem, et al.
Desalination and Water Treatment (2024) Vol. 318, pp. 100344-100344
Open Access | Times Cited: 10
Glass hardness: Predicting composition and load effects via symbolic reasoning-informed machine learning
Sajid Mannan, Mohd Zaki, Suresh Bishnoi, et al.
Acta Materialia (2023) Vol. 255, pp. 119046-119046
Open Access | Times Cited: 17
Influence of Al addition on the microstructure and properties of Fe25Co25Ni25(Si0.3B0.7)25 high entropy metallic glass
Shuyan Zhang, Danyue Ma, Xiubing Liang, et al.
Intermetallics (2024) Vol. 169, pp. 108304-108304
Closed Access | Times Cited: 5
Advances in modeling complex materials: The rise of neuroevolution potentials
Penghua Ying, Qian Cheng, Rui Zhao, et al.
Chemical Physics Reviews (2025) Vol. 6, Iss. 1
Open Access
Tribological Properties Assessment of Metallic Glasses Through a Genetic Algorithm-Optimized Machine Learning Model
Untung Rahardja, Arif Sarı, Ali Alsalamy, et al.
Metals and Materials International (2023) Vol. 30, Iss. 3, pp. 745-755
Closed Access | Times Cited: 13
Viscosity and melting temperature prediction of mold fluxes based on explainable machine learning and SHapley additive exPlanations
Wei Yan, Yangyang Shen, Shoujie Chen, et al.
Journal of Non-Crystalline Solids (2024) Vol. 636, pp. 123037-123037
Closed Access | Times Cited: 4
Convolutional neural network-based prediction of hardness in bulk metallic glasses with small data
Chunghee Nam
Journal of Non-Crystalline Solids (2025) Vol. 654, pp. 123451-123451
Closed Access
Convolutional neural network guided prediction of saturation magnetic flux density of Fe-based metallic glasses
Chunghee Nam
Computational Materials Science (2023) Vol. 225, pp. 112198-112198
Closed Access | Times Cited: 8
Prediction of magnetocaloric properties of Fe-based amorphous alloys based on interpretable machine learning
Chengcheng Liu, Xuandong Wang, Weidong Cai, et al.
Journal of Non-Crystalline Solids (2023) Vol. 625, pp. 122749-122749
Closed Access | Times Cited: 7
Data-driven machine learning prediction of flow curve and Newtonian to non-Newtonian flow regime transition in bulk metallic glasses within supercooled liquid region
Pouria Mohammad, Mehdi Malekan, Alireza Jalali
Journal of Non-Crystalline Solids (2024) Vol. 627, pp. 122839-122839
Closed Access | Times Cited: 2
Prediction and Rational Design of Stacking Fault Energy of Austenitic Alloys Based on Interpretable Machine Learning and Chemical Composition
Chengcheng Liu, Hang Su
steel research international (2024) Vol. 95, Iss. 8
Closed Access | Times Cited: 2
Prediction of glass transition temperature of oxide glasses based on interpretable machine learning and sparse data sets
Chengcheng Liu, Hang Su
Materials Today Communications (2024) Vol. 40, pp. 109691-109691
Closed Access | Times Cited: 2
A micromechanical nested machine learning model for characterizing materials behaviors of bulk metallic glasses
Moustafa Sahnoune Chaouche, Hani K. Al-Mohair, Shavan Askar, et al.
Journal of Non-Crystalline Solids (2023) Vol. 625, pp. 122733-122733
Closed Access | Times Cited: 6
Prediction and customized design of Curie temperature of Fe-based amorphous alloys based on interpretable machine learning
Chengcheng Liu, Yongchao Lu, Jianfa Feng, et al.
Materials Today Communications (2023) Vol. 38, pp. 107667-107667
Closed Access | Times Cited: 6
Predicting the Average Composition of an AlFeNiTiVZr-Cr Alloy with Machine Learning and X-ray Spectroscopy
Tarik Sadat
Compounds (2023) Vol. 3, Iss. 1, pp. 224-232
Open Access | Times Cited: 5
Length-scale effect on the hardness of metallic/ceramic multilayered composites: A machine learning prediction
Ming Wang, Ying Chen, C.L. Zhang, et al.
Scripta Materialia (2023) Vol. 242, pp. 115921-115921
Closed Access | Times Cited: 5
An end-to-end explainable graph neural networks-based composition to mechanical properties prediction framework for bulk metallic glasses
Tao Long, Zhilin Long, Bo Pang
Mechanics of Materials (2024) Vol. 191, pp. 104945-104945
Closed Access | Times Cited: 1
Prediction of digital transformation of manufacturing industry based on interpretable machine learning
Chen Zhu, Xue Liu, Dong Chen
PLoS ONE (2024) Vol. 19, Iss. 3, pp. e0299147-e0299147
Open Access | Times Cited: 1
Efficient learning strategy for predicting glass forming ability in imbalanced datasets of bulk metallic glasses
Xuhe Gong, Jiazi Bi, Xiaobin Liu, et al.
Physical Review Materials (2024) Vol. 8, Iss. 5
Open Access | Times Cited: 1
An interpretable predictive framework based on rules and machine learning: rule extraction, validation and adaptive integration with machine learning
Huiran Zhang, Mengmeng Dai, Yudian Lin, et al.
Research Square (Research Square) (2024)
Open Access
Machine Learning Design of Aluminum-Lithium Alloys with High Strength
Hongxia Wang, Zhiqiang Duan, Qingwei Guo, et al.
Computers, materials & continua/Computers, materials & continua (Print) (2023) Vol. 77, Iss. 2, pp. 1393-1409
Open Access
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