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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:

Predictions and Strategies Learned from Machine Learning to Develop High‐Performing Perovskite Solar Cells
Jinxin Li, Basudev Pradhan, Surya Gaur, et al.
Advanced Energy Materials (2019) Vol. 9, Iss. 46
Closed Access | Times Cited: 152

Showing 26-50 of 152 citing articles:

Topological feature engineering for machine learning based halide perovskite materials design
D. Vijay Anand, Qiang Xu, JunJie Wee, et al.
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 40
Band gap predictions of double perovskite oxides using machine learning
Anjana Talapatra, Blas P. Uberuaga, Christopher R. Stanek, et al.
Communications Materials (2023) Vol. 4, Iss. 1
Open Access | Times Cited: 38
Machine learning for perovskite solar cell design
Hui Zhan, Min Wang, Xiang Yin, et al.
Computational Materials Science (2023) Vol. 226, pp. 112215-112215
Closed Access | Times Cited: 28
Machine Learning for Halide Perovskite Materials ABX3 (B = Pb, X = I, Br, Cl) Assessment of Structural Properties and Band Gap Engineering for Solar Energy
Afnan Alhashmi, Mohammed Benali Kanoun, Souraya Goumri‐Said
Materials (2023) Vol. 16, Iss. 7, pp. 2657-2657
Open Access | Times Cited: 22
Engineering and Design of Halide Perovskite Photoelectrochemical Cells for Solar‐Driven Water Splitting
Saikiran Khamgaonkar, Anny Leudjo Taka, Vivek Maheshwari
Advanced Functional Materials (2024) Vol. 34, Iss. 46
Open Access | Times Cited: 14
The Future of Material Scientists in an Age of Artificial Intelligence
Ayman Maqsood, Chen Chen, T. Jesper Jacobsson
Advanced Science (2024) Vol. 11, Iss. 19
Open Access | Times Cited: 12
Exploring KGeCl3 material for perovskite solar cell absorber layer through different machine learning models
Nikhil Shrivastav, Mir Aamir Hamid, Jaya Madan, et al.
Solar Energy (2024) Vol. 278, pp. 112784-112784
Closed Access | Times Cited: 8
Deep learning for augmented process monitoring of scalable perovskite thin-film fabrication
Felix Laufer, Markus Götz, Ulrich W. Paetzold
Energy & Environmental Science (2025)
Open Access | Times Cited: 1
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
High-performance quasi-2D perovskite solar cells with power conversion efficiency over 20% fabricated in humidity-controlled ambient air
Xue Lai, Wenhui Li, Xiaoyu Gu, et al.
Chemical Engineering Journal (2021) Vol. 427, pp. 130949-130949
Closed Access | Times Cited: 45
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: 37
Discovery of direct band gap perovskites for light harvesting by using machine learning
Smarak Rath, G. Sudha Priyanga, N. Nagappan, et al.
Computational Materials Science (2022) Vol. 210, pp. 111476-111476
Closed Access | Times Cited: 35
Identifying correlation between the open-circuit voltage and the frontier orbital energies of non-fullerene organic solar cells based on interpretable machine-learning approaches
Min‐Hsuan Lee
Solar Energy (2022) Vol. 234, pp. 360-367
Closed Access | Times Cited: 32
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
High-throughput calculation and machine learning of two-dimensional halide perovskite materials: Formation energy and band gap
Wenguang Hu, Lei Zhang
Materials Today Communications (2023) Vol. 35, pp. 105841-105841
Closed Access | Times Cited: 17
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: 16
Interpretable machine learning predictions for efficient perovskite solar cell development
Jinghao Hu, Zhengxin Chen, Yuzhi Chen, et al.
Solar Energy Materials and Solar Cells (2024) Vol. 271, pp. 112826-112826
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 Strategies to Develop High Efficiency Indoor Perovskite Solar Cells
Snehangshu Mishra, Sangratna Baburao Gaikwad, Trilok Singh
Advanced Theory and Simulations (2024) Vol. 7, Iss. 5
Closed Access | Times Cited: 6
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: 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
Development of all-inorganic lead halide perovskites for carbon dioxide photoreduction
Suverna Trivedi, Daniel Prochowicz, Abul Kalam, et al.
Renewable and Sustainable Energy Reviews (2021) Vol. 145, pp. 111047-111047
Closed Access | Times Cited: 35
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: 15
Advancing vapor-deposited perovskite solar cellsviamachine learning
Jiazheng Wang, Yuchen Qi, Haofeng Zheng, et al.
Journal of Materials Chemistry A (2023) Vol. 11, Iss. 25, pp. 13201-13208
Closed Access | Times Cited: 14
Perovskite-quantum dot hybrid solar cells: a multi-win strategy for high performance and stability
Ke Huang, Junlong Liu, Jianjuan Yuan, et al.
Journal of Materials Chemistry A (2023) Vol. 11, Iss. 9, pp. 4487-4509
Closed Access | Times Cited: 13

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