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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:

Advances of machine learning in materials science: Ideas and techniques
Sue Sin Chong, Yi Sheng Ng, Hui‐Qiong Wang, et al.
Frontiers of Physics (2023) Vol. 19, Iss. 1
Open Access | Times Cited: 24

Showing 24 citing articles:

Development of an Analytical Model for Predicting the Shear Viscosity of Polypropylene Compounds
Lukas Seifert, Lisa Leuchtenberger-Engel, Lukas Seifert
Polymers (2025) Vol. 17, Iss. 2, pp. 126-126
Open Access | Times Cited: 1
Deep learning in two-dimensional materials: Characterization, prediction, and design
Xinqin Meng, Chengbing Qin, Xilong Liang, et al.
Frontiers of Physics (2024) Vol. 19, Iss. 5
Open Access | Times Cited: 5
Enhanced predictive optimization of methane dry reforming via ResponseSurface methodology and artificial neural network approaches: Insights using a novel nickel-strontium-zirconium-aluminum catalyst
Tahani S. Gendy, Radwa A. El‐Salamony, Maher M. Alrashed, et al.
Molecular Catalysis (2024) Vol. 562, pp. 114216-114216
Open Access | Times Cited: 4
Low-dimensional N-heterocyclic carbenes nanomaterials: Promising supports of single atom catalysts
Wei Liu, Sitong Liu, Kai Guo, et al.
Vacuum (2024) Vol. 227, pp. 113390-113390
Closed Access | Times Cited: 4
Application of machine learning in polyimide structure design and property regulation
Wenjia Huo, Haiyue Wang, Liying Guo, et al.
High Performance Polymers (2025)
Closed Access
Machine Learning-Driven Prediction of Composite Materials Properties Based on Experimental Testing Data
Kristina Berladir, Katarzyna Antosz, Vitalii Ivanov, et al.
Polymers (2025) Vol. 17, Iss. 5, pp. 694-694
Open Access
The Role of Artificial Intelligence in Microstructural Characterization of Ferrous Materials: A Review of Tools, Findings, and Research Challenges
Naveed Anjum, Suma, M. Rakesh, et al.
Journal of Mines Metals and Fuels (2025), pp. 759-769
Closed Access
Detection of COVID-19, lung opacity, and viral pneumonia via X-ray using machine learning and deep learning
Hajar Lamouadene, Majid EL Kassaoui, M. El Yadari, et al.
Computers in Biology and Medicine (2025) Vol. 191, pp. 110131-110131
Open Access
Development of PP Compound Recipes Using Genetic Algorithms and Analytical Models
Lukas Seifert, Lisa Leuchtenberger-Engel, Christian Hopmann
Polymers (2025) Vol. 17, Iss. 8, pp. 1059-1059
Open Access
Local symmetry-driven interfacial magnetization and electronic states in (ZnO)n/(w-FeO)n superlattices
Jiaxin Gao, Yi Sheng Ng, Hao Cheng, et al.
Physical Chemistry Chemical Physics (2024) Vol. 26, Iss. 15, pp. 12084-12096
Open Access | Times Cited: 2
Universal scales of electronegativity and ionicity from electron scattering factors
Jin‐Cheng Zheng
Physical Chemistry Chemical Physics (2024) Vol. 26, Iss. 29, pp. 19705-19723
Closed Access | Times Cited: 2
Machine Learning-Based Prediction of Stability in High-Entropy Nitride Ceramics
Tianyu Lin, Ruolan Wang, Dazhi Liu
Crystals (2024) Vol. 14, Iss. 5, pp. 429-429
Open Access | Times Cited: 1
Interfacial electronic state between hexagonal ZnO and cubic NiO
Yii Yat Chan, Zi Cheng Tey, Hui‐Qiong Wang
RSC Advances (2024) Vol. 14, Iss. 24, pp. 17238-17244
Open Access | Times Cited: 1
Artificial intelligence modeling for power system planning
Sonja Knežević, Mileta Žarković
Electrical Engineering (2024)
Open Access | Times Cited: 1
State-of-the-art review on various applications of machine learning techniques in materials science and engineering
Bing Yu, Lai‐Chang Zhang, Xiaoxia Ye, et al.
Chemical Engineering Science (2024), pp. 121147-121147
Closed Access | Times Cited: 1
Prediction of the Tribological Properties of Graphite-Filled Ptfe Composites Based on Machine Learning Algorithms
Huifeng Ning, Wenwen WANG, Gui Gao, et al.
(2024)
Closed Access
A machine learning approach for dynamical modelling of Al distributions in zeolites via 23Na/27Al solid-state NMR
Lei Chen, Carlos Bornes, Oscar Bengtsson, et al.
Faraday Discussions (2024)
Open Access
Analysing the Roles of Robotics in Manufacturing Organizations in the Era of Industry 4.0
Adedotun Adetunla, Esther T. Akinlabi, Tien‐Chien Jen, et al.
(2024), pp. 1-5
Closed Access
Harnessing the Power of Artificial Intelligence in Materials Science: An Overview
Adedotun Adetunla, Esther T. Akinlabi, Tien‐Chien Jen, et al.
(2024), pp. 1-6
Closed Access
Multi-iteration active learning for the composition design of potassium–sodium niobate ceramics with enhanced piezoelectric coefficient
Heng Hu, Miaomiao Huang, Bin Wang, et al.
Ceramics International (2024)
Closed Access
From Sunlight to Solutions: Closing the Loop on Halide Perovskites
Elham Foadian, Sheryl L. Sanchez, Sergei V. Kalinin, et al.
ACS Materials Au (2024) Vol. 5, Iss. 1, pp. 11-23
Open Access
Development of an Analytical Model for Predicting the Tensile Modulus of Complex Polypropylene Compounds
Lukas Seifert, Lisa Leuchtenberger-Engel, Lukas Seifert
Polymers (2024) Vol. 16, Iss. 23, pp. 3403-3403
Open Access
Analysis on Mechanical Behavior of Additively Manufactured PLA/Eggshell Composites Using Machine Learning Algorithms
Nisha Soms, K. Ravikumar, N. Gunasekar
Journal of Materials Engineering and Performance (2024)
Closed Access
Machine learning prediction of materials properties from chemical composition: Status and prospects
Mohammed Alghadeer, Nyimas Aisyah, Mahmoud Hezam, et al.
Chemical Physics Reviews (2024) Vol. 5, Iss. 4
Open Access
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