Advancing perovskite photovoltaic technology through machine learning‐driven automation

Advancing perovskite photovoltaic technology through machine learning‐driven automation

Abstract Since its emergence in 2009, perovskite photovoltaic technology has achieved remarkable progress, with efficiencies soaring from 3.8% to over 26%. Despite these advancements, challenges such as long‐term material and device stability remain. Addressing these challenges requires reproducible...

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Bibliographic Details
Main Authors: Jiyun Zhang, Jianchang Wu, Vincent M. Le Corre, Jens A. Hauch, Yicheng Zhao, Christoph J. Brabec
Format: Article
Language:English
Published: Wiley 2025-05-01
Series:InfoMat
Subjects:
automation
device acceleration platforms
machine learning
materials acceleration platforms
perovskite solar cells
self‐driving laboratory
Online Access:https://doi.org/10.1002/inf2.70005
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https://doi.org/10.1002/inf2.70005

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