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 in Enhancing Corrosion Resistance of Magnesium Alloys: A Comprehensive Review
Yanbing Guo, Mingze Sun, Wang Zhang, et al.
Metals (2023) Vol. 13, Iss. 10, pp. 1790-1790
Open Access | Times Cited: 7
Yanbing Guo, Mingze Sun, Wang Zhang, et al.
Metals (2023) Vol. 13, Iss. 10, pp. 1790-1790
Open Access | Times Cited: 7
Showing 7 citing articles:
Prediction of corrosion rate for friction stir processed WE43 alloy by combining PSO-based virtual sample generation and machine learning
Annayath Maqbool, Abdul Khalad, Noor Zaman Khan
Journal of Magnesium and Alloys (2024) Vol. 12, Iss. 4, pp. 1518-1528
Open Access | Times Cited: 9
Annayath Maqbool, Abdul Khalad, Noor Zaman Khan
Journal of Magnesium and Alloys (2024) Vol. 12, Iss. 4, pp. 1518-1528
Open Access | Times Cited: 9
Machine learning-guided accelerated discovery of structure-property correlations in lean magnesium alloys for biomedical applications
Sreenivas Raguraman, Maitreyee Sharma Priyadarshini, Tram Nguyen, et al.
Journal of Magnesium and Alloys (2024) Vol. 12, Iss. 6, pp. 2267-2283
Open Access | Times Cited: 7
Sreenivas Raguraman, Maitreyee Sharma Priyadarshini, Tram Nguyen, et al.
Journal of Magnesium and Alloys (2024) Vol. 12, Iss. 6, pp. 2267-2283
Open Access | Times Cited: 7
Rapid prediction of the corrosion behaviour of coated biodegradable magnesium alloys using phase field simulation and machine learning
Songyun Ma, Dawei Zhang, Peilei Zhang, et al.
Computational Materials Science (2024) Vol. 247, pp. 113546-113546
Open Access | Times Cited: 3
Songyun Ma, Dawei Zhang, Peilei Zhang, et al.
Computational Materials Science (2024) Vol. 247, pp. 113546-113546
Open Access | Times Cited: 3
State-of-the-art progress on artificial intelligence and machine learning in accessing molecular coordination and adsorption of corrosion inhibitors
Taiwo W. Quadri, Ekemini D. Akpan, Saheed E. Elugoke, et al.
Applied Physics Reviews (2025) Vol. 12, Iss. 1
Closed Access
Taiwo W. Quadri, Ekemini D. Akpan, Saheed E. Elugoke, et al.
Applied Physics Reviews (2025) Vol. 12, Iss. 1
Closed Access
Using Artificial Neural Networks in Designing and Developing Magnesium-Based Materials for Degradable Implant Applications
Manoj Kumar Elipey, P.S. Kishore, Ratna Sunil Buradagunta
Advances in chemical and materials engineering book series (2025), pp. 231-244
Closed Access
Manoj Kumar Elipey, P.S. Kishore, Ratna Sunil Buradagunta
Advances in chemical and materials engineering book series (2025), pp. 231-244
Closed Access
Ignition-proof magnesium alloys with rare earth elements: A novel framework to predict combustion phases, surface morphologies, and hidden features using heating rates
Muhammad Zeeshan Farooq, Yiyong Wu, M.Y. Zheng, et al.
Journal of Magnesium and Alloys (2025)
Open Access
Muhammad Zeeshan Farooq, Yiyong Wu, M.Y. Zheng, et al.
Journal of Magnesium and Alloys (2025)
Open Access
Corrosion modeling of Magnesium and its alloys: A short review
A. Ortiz-Ozuna, Marvin Montoya-Rangel, Homero Castaneda, et al.
Materials Today Communications (2025), pp. 112491-112491
Open Access
A. Ortiz-Ozuna, Marvin Montoya-Rangel, Homero Castaneda, et al.
Materials Today Communications (2025), pp. 112491-112491
Open Access
