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Cited article:

Machine learning-accelerated prediction of the energy yield and identification of the governing loss feature for perovskite/silicon tandem solar cells under real solar spectra

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Solar Energy Materials and Solar Cells 306 114542 (2026)
https://doi.org/10.1016/j.solmat.2026.114542

Materials and Device Engineering for Efficient, Stable, and Scalable Monolithic Perovskite/Silicon Tandem Photovoltaics

Lorenzo Mardegan, Ahmed Ali Said, Anil R. Pininti, Thomas Allen, Anand S. Subbiah and Stefaan De Wolf
Chemical Reviews 126 (14) 7959 (2026)
https://doi.org/10.1021/acs.chemrev.5c01014

Improving the Comprehensive Modeling Framework for Energy Yield Simulations of Various Novel Photovoltaic Systems

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Advanced Theory and Simulations 9 (5) (2026)
https://doi.org/10.1002/adts.202501793

Prospects for Climate-Specific Design of Perovskite-Silicon Tandem Solar Cells and the Influence of Degradation Rates

Karthik Raitani and Pradeep R. Nair
ACS Applied Energy Materials 8 (19) 14141 (2025)
https://doi.org/10.1021/acsaem.5c01634