Home /Research /Machine Learning Enhanced High‐Throughput Fabrication and Optimization of Quasi‐2D Ruddlesden–Popper Perovskite Solar Cells (Adv. Energy Mater. 38/2023)

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Machine Learning Enhanced High‐Throughput Fabrication and Optimization of Quasi‐2D Ruddlesden–Popper Perovskite Solar Cells (Adv. Energy Mater. 38/2023)

Nastaran Meftahi, Maciej Adam Surmiak, ‪Sebastian O. Fürer, Kevin J. Rietwyk, Jianfeng Lu, Sonia R. Raga, C.C. Evans, Monika Michalska, Hao Deng, David P. McMeekin, Tuncay Alan, Doojin Vak, Anthony S. R. Chesman, Andrew J. Christofferson, David A. Winkler, Salvy P. Russo

Year: 2023
Citations: 3
Access: Open access

Abstract

Perovskite Solar Cells In article number 2203859, Nastaran Meftahi, Maciej Adam Surmiak, Andrew J. Christofferson, and co-workers present a methodology for efficiently exploring the vast compositional space of quasi-2D Ruddlesden-Popper perovskite solar cells using a combination of machine learning and a reproducible, combinatorial high-throughput robotic fabrication process. This methodology provides a platform for further optimization of solar cell power conversion efficiency and stability.

Keywords

FabricationMaterials sciencePerovskite (structure)ThroughputEnergy conversion efficiencyNanotechnologyEnergy transformationChemical engineeringOptoelectronicsComputer science

Machine Learning Enhanced High‐Throughput Fabrication and Optimization of Quasi‐2D Ruddlesden–Popper Perovskite Solar Cells (Adv. Energy Mater. 38/2023)

Abstract

Keywords

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