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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:

Performance analysis of perovskite solar cells in 2013–2018 using machine-learning tools
Çağla Odabaşı, Ramazan Yıldırım
Nano Energy (2018) Vol. 56, pp. 770-791
Closed Access | Times Cited: 116

Showing 26-50 of 116 citing articles:

Simple, Robust, and Going More Efficient: Recent Advance on Electron Transport Layer‐Free Perovskite Solar Cells
Like Huang, Ziyi Ge
Advanced Energy Materials (2019) Vol. 9, Iss. 24
Closed Access | Times Cited: 73
Global discovery of stable and non-toxic hybrid organic-inorganic perovskites for photovoltaic systems by combining machine learning method with first principle calculations
Tianmin Wu, Jian Wang
Nano Energy (2019) Vol. 66, pp. 104070-104070
Closed Access | Times Cited: 73
Novel approaches and scalability prospects of copper based hole transporting materials for planar perovskite solar cells
Maria Bidikoudi, Emmanuel Kymakis
Journal of Materials Chemistry C (2019) Vol. 7, Iss. 44, pp. 13680-13708
Open Access | Times Cited: 63
A dopant-free twisted organic small-molecule hole transport material for inverted planar perovskite solar cells with enhanced efficiency and operational stability
Xiaolong Yang, Jun Xi, Yuanhui Sun, et al.
Nano Energy (2019) Vol. 64, pp. 103946-103946
Closed Access | Times Cited: 56
Assessment of critical materials and cell design factors for high performance lithium-sulfur batteries using machine learning
Aysegul Kilic, Çağla Odabaşı, Ramazan Yıldırım, et al.
Chemical Engineering Journal (2020) Vol. 390, pp. 124117-124117
Closed Access | Times Cited: 50
Enhancing the stability of organic photovoltaics through machine learning
Tudur Wyn David, Helder Scapin Anizelli, T. Jesper Jacobsson, et al.
Nano Energy (2020) Vol. 78, pp. 105342-105342
Open Access | Times Cited: 50
Is machine learning redefining the perovskite solar cells?
Nishi Parikh, Meera Karamta, Neha Yadav, et al.
Journal of Energy Chemistry (2021) Vol. 66, pp. 74-90
Closed Access | Times Cited: 44
Data-driven design of high-performance MASnxPb1-xI3 perovskite materials by machine learning and experimental realization
Xia Cai, Fengcai Liu, Anran Yu, et al.
Light Science & Applications (2022) Vol. 11, Iss. 1
Open Access | Times Cited: 38
Challenges, Opportunities, and Prospects in Metal Halide Perovskites from Theoretical and Machine Learning Perspectives
Chang Woo Myung, Amir Hajibabaei, Ji‐Hyun Cha, et al.
Advanced Energy Materials (2022) Vol. 12, Iss. 45
Closed Access | Times Cited: 33
Machine learning utilized for the development of proton exchange membrane electrolyzers
Rui Ding, Yawen Chen, Zhiyan Rui, et al.
Journal of Power Sources (2022) Vol. 556, pp. 232389-232389
Closed Access | Times Cited: 28
Selecting an appropriate machine-learning model for perovskite solar cell datasets
Mohamed M. Salah, Zahraa Ismail, Sameh O. Abdellatif
Materials for Renewable and Sustainable Energy (2023) Vol. 12, Iss. 3, pp. 187-198
Open Access | Times Cited: 21
The role of machine learning in perovskite solar cell research
Chen Chen, Ayman Maqsood, T. Jesper Jacobsson
Journal of Alloys and Compounds (2023) Vol. 960, pp. 170824-170824
Closed Access | Times Cited: 19
A state-of-the-art review on the utilization of machine learning in nanofluids, solar energy generation, and the prognosis of solar power
Santosh Kumar Singh, Arun Kumar Tiwari, H.K. Paliwal
Engineering Analysis with Boundary Elements (2023) Vol. 155, pp. 62-86
Closed Access | Times Cited: 17
A Comprehensive Review of the Application of Machine Learning in Fabrication and Implementation of Photovoltaic Systems
Srabanti Datta, Anik Baul, Gobinda Chandra Sarker, et al.
IEEE Access (2023) Vol. 11, pp. 77750-77778
Open Access | Times Cited: 17
Perovskites informatics: Studying the impact of thicknesses, doping, and defects on the perovskite solar cell efficiency using a machine learning algorithm
Zahraa Ismail, Eman F. Sawires, Fathy Z. Amer, et al.
International Journal of Numerical Modelling Electronic Networks Devices and Fields (2023) Vol. 37, Iss. 2
Closed Access | Times Cited: 17
Stability forecasting of perovskite solar cells utilizing various machine learning and deep learning techniques
M. Mammeri, H. Bencherif, L. Dehimi, et al.
Journal of Optics (2024)
Closed Access | Times Cited: 7
Data‐Driven Tunnel Oxide Passivated Contact Solar Cell Performance Analysis Using Machine Learning
Jiakai Zhou, T. Jesper Jacobsson, Zhi Wang, et al.
Advanced Materials (2024) Vol. 36, Iss. 14
Closed Access | Times Cited: 6
Machine learning guided efficiency improvement for Sn-based perovskite solar cells with efficiency exceeding 20%
Weiyin Gao, Chenxin Ran, Liang Zhao, et al.
Rare Metals (2024) Vol. 43, Iss. 11, pp. 5720-5733
Closed Access | Times Cited: 6
Water in hybrid perovskites: Bulk MAPbI3 degradation via super-hydrous state
Arvin Kakekhani, Radhika N. Katti, Andrew M. Rappe
APL Materials (2019) Vol. 7, Iss. 4
Open Access | Times Cited: 44
Machine learning-guided search for high-efficiency perovskite solar cells with doped electron transport layers
Chenglong She, Qicheng Huang, Cong Chen, et al.
Journal of Materials Chemistry A (2021) Vol. 9, Iss. 44, pp. 25168-25177
Closed Access | Times Cited: 37
Recent progress in the data-driven discovery of novel photovoltaic materials
Lu Tian, Minjie Li, Wencong Lu, et al.
Journal of Materials Informatics (2022) Vol. 2, Iss. 2, pp. 7-7
Open Access | Times Cited: 25
Revolutionizing Low‐Cost Solar Cells with Machine Learning: A Systematic Review of Optimization Techniques
Satyam Bhatti, Habib Ullah Manzoor, Bruno Michel, et al.
Advanced Energy and Sustainability Research (2023) Vol. 4, Iss. 10
Open Access | Times Cited: 15
Application of large datasets to assess trends in the stability of perovskite photovoltaics through machine learning
Bashayer Nafe N Alsulami, Tudur Wyn David, A Essien, et al.
Journal of Materials Chemistry A (2024) Vol. 12, Iss. 5, pp. 3122-3132
Open Access | Times Cited: 5
Assessment of Reproducibility, Hysteresis, and Stability Relations in Perovskite Solar Cells Using Machine Learning
Çağla Odabaşı, Ramazan Yıldırım
Energy Technology (2020) Vol. 8, Iss. 12
Closed Access | Times Cited: 38
Naphthalene imide dimer as interface engineering material: An efficient strategy for achieving high-performance perovskite solar cells
Helin Wang, Fan Zhang, Zhuohua Li, et al.
Chemical Engineering Journal (2020) Vol. 395, pp. 125062-125062
Open Access | Times Cited: 37

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