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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:

Applied Machine Learning for Developing Next‐Generation Functional Materials
Filip Dinic, Kamalpreet Singh, Tony Dong, et al.
Advanced Functional Materials (2021) Vol. 31, Iss. 51
Closed Access | Times Cited: 41

Showing 1-25 of 41 citing articles:

Mechanical metamaterials and beyond
Pengcheng Jiao, J. Howard Mueller, Jordan R. Raney, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 219
Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models
Vijendra Kumar, Naresh Kedam, Kul Vaibhav Sharma, et al.
Water (2023) Vol. 15, Iss. 14, pp. 2572-2572
Open Access | Times Cited: 103
A Rising 2D Star: Novel MBenes with Excellent Performance in Energy Conversion and Storage
Tianjie Xu, Yuhua Wang, Zu-zhao Xiong, et al.
Nano-Micro Letters (2022) Vol. 15, Iss. 1
Open Access | Times Cited: 93
Machine-Learning-Assisted Screening of Interface Passivation Materials for Perovskite Solar Cells
Chongyang Zhi, Suo Wang, Shijing Sun, et al.
ACS Energy Letters (2023) Vol. 8, Iss. 3, pp. 1424-1433
Closed Access | Times Cited: 47
Recent advancements on designing transition metal-based carbon-supported single atom catalysts for oxygen electrocatalysis: Miles to go for sustainable Zn-air batteries
Srijib Das, Aniruddha Kundu, Tapas Kuila, et al.
Energy storage materials (2023) Vol. 61, pp. 102890-102890
Closed Access | Times Cited: 44
Scalable deeper graph neural networks for high-performance materials property prediction
Sadman Sadeed Omee, Steph-Yves Louis, Nihang Fu, et al.
Patterns (2022) Vol. 3, Iss. 5, pp. 100491-100491
Open Access | Times Cited: 68
How machine learning can accelerate electrocatalysis discovery and optimization
Stephan N. Steinmann, Qing Wang, Zhi Wei Seh
Materials Horizons (2022) Vol. 10, Iss. 2, pp. 393-406
Open Access | Times Cited: 52
Data-driven machine learning approach for predicting the capacitance of graphene-based supercapacitor electrodes
Ahmed G. Saad, Ahmed Emad-Eldeen, Wael Z. Tawfik, et al.
Journal of Energy Storage (2022) Vol. 55, pp. 105411-105411
Closed Access | Times Cited: 43
Machine learning-guided design of organic phosphorus-containing flame retardants to improve the limiting oxygen index of epoxy resins
Zhongwei Chen, Boran Yang, Nannan Song, et al.
Chemical Engineering Journal (2022) Vol. 455, pp. 140547-140547
Open Access | Times Cited: 39
Mechanical energy metamaterials in interstellar travel
Pengcheng Jiao
Progress in Materials Science (2023) Vol. 137, pp. 101132-101132
Closed Access | Times Cited: 33
Machine Learning Assisted Graphdiyne-Based Nanozyme Discovery
Yixin Yu, Yujie Jiang, Chaohui Zhang, et al.
ACS Materials Letters (2022) Vol. 4, Iss. 11, pp. 2134-2142
Closed Access | Times Cited: 30
Recent advance on machine learning of MXenes for energy storage and conversion
Chengfei Qian, Kaiwen Sun, Weizhai Bao
International Journal of Energy Research (2022) Vol. 46, Iss. 15, pp. 21511-21522
Closed Access | Times Cited: 29
Artificial intelligence-driven rechargeable batteries in multiple fields of development and application towards energy storage
Zheng Li, Shuqing Zhang, Hao Huang, et al.
Journal of Energy Storage (2023) Vol. 73, pp. 108926-108926
Closed Access | Times Cited: 17
Machine Learning Accelerated Discovery of Covalent Organic Frameworks for Environmental and Energy Applications
Hao Wang, Yuquan Li, Xiaoyang Xuan, et al.
Environmental Science & Technology (2025)
Closed Access
Building Smarter Aqueous Batteries
Canbin Deng, Yiqing Li, Jiaqiang Huang
Small Methods (2023) Vol. 8, Iss. 6
Open Access | Times Cited: 11
Machine learning-based screening of two-dimensional perovskite organic spacers
Yongxiang Mai, Jianyao Tang, Haogang Meng, et al.
Advanced Composites and Hybrid Materials (2024) Vol. 7, Iss. 3
Closed Access | Times Cited: 4
Accelerating the design and discovery of tribocorrosion-resistant metals by interfacing multiphysics modeling with machine learning and genetic algorithms
Yucong Gu, Kaiwen Wang, Zhengyu Zhang, et al.
npj Materials Degradation (2025) Vol. 9, Iss. 1
Open Access
Advanced theoretical modeling methodologies for electrocatalyst design in sustainable energy conversion
Tianyi Wang, Qilong Wu, Yun Han, et al.
Applied Physics Reviews (2025) Vol. 12, Iss. 1
Open Access
Machine learning-assisted design and prediction of materials for batteries based on alkali metals
Ke Si, Zhipeng Sun, Huaxin Song, et al.
Physical Chemistry Chemical Physics (2025)
Closed Access
Machine learning-assisted structure design and materials screening of a bilayer thin-film selective thermal emitter for infrared stealth technology
Shengtong Niu, Zhiyu Ren, Chenchong Wang, et al.
Chemical Engineering Journal (2025), pp. 161418-161418
Closed Access
GCPNet: An interpretable Generic Crystal Pattern graph neural Network for predicting material properties
H. Gao, Xiaowei Guo, Genglin Li, et al.
Neural Networks (2025) Vol. 188, pp. 107466-107466
Closed Access
Formula Graph Self‐Attention Network for Representation‐Domain Independent Materials Discovery
Achintha Ihalage, Yang Hao
Advanced Science (2022) Vol. 9, Iss. 18
Open Access | Times Cited: 17
Machine learning for expediting next-generation of fire-retardant polymer composites
Pooya Jafari, Ruoran Zhang, Siqi Huo, et al.
Composites Communications (2023) Vol. 45, pp. 101806-101806
Open Access | Times Cited: 9
Perspectives on Development of Optoelectronic Materials in Artificial Intelligence Age
Ting Yuan, Xianzhi Song, Yuxin Shi, et al.
Chemistry - An Asian Journal (2024) Vol. 19, Iss. 6
Closed Access | Times Cited: 3
Unconstrained Machine Learning Screening for New Li‐Ion Cathode Materials Enhanced by Class Balancing
Filip Dinic, Oleksandr Voznyy
Advanced Theory and Simulations (2023) Vol. 6, Iss. 6
Open Access | Times Cited: 7
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