Deep learning for augmented process monitoring of scalable perovskite thin-film fabrication
Energy Environ. Sci., 2025, 18,1767-1782DOI: 10.1039/D4EE03445G, Paper Open Access   This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.Felix Laufer, Markus Götz, Ulrich W. PaetzoldAugmenting characterization methods with deep learning and other machine learning methods allows the identification of material inconsistencies, device performance predictions, and the generation of in situ AI recommendations.The content of this RSS Feed (c) The Royal Society of Chemistry
Energy Environ. Sci., 2025, 18,1767-1782
DOI: 10.1039/D4EE03445G, Paper
DOI: 10.1039/D4EE03445G, Paper
Open Access
This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.Felix Laufer, Markus Götz, Ulrich W. Paetzold
Augmenting characterization methods with deep learning and other machine learning methods allows the identification of material inconsistencies, device performance predictions, and the generation of in situ AI recommendations.
The content of this RSS Feed (c) The Royal Society of Chemistry
Augmenting characterization methods with deep learning and other machine learning methods allows the identification of material inconsistencies, device performance predictions, and the generation of in situ AI recommendations.
The content of this RSS Feed (c) The Royal Society of Chemistry
