Drift-diffusion and Machine Learning for High Efficiency Perovskite-Perovskite based Tandem Solar Cells

Reference : Drift-diffusion and Machine Learning for High Efficiency Perovskite-Perovskite based ...


	Document type :	Scientific congresses, symposiums and conference proceedings : Poster
	Discipline(s) :	Physical, chemical, mathematical & earth Sciences : Physics
	Focus Areas :	Physics and Materials Science
	To cite this reference:	http://hdl.handle.net/10993/47900

Title :	Drift-diffusion and Machine Learning for High Efficiency Perovskite-Perovskite based Tandem Solar Cells
Language :	English
Author, co-author :	Singh, Ajay [Department of Electrical and Computer Engineering, Technical University of Munich]
	Gagliardi, Alessio [Department of Electrical and Computer Engineering, Technical University of Munich]
Publication date :	May-2019
Peer reviewed :	Yes
Audience :	International
Event name :	Proceedings of International Conference on Hybrid and Organic Photovoltaics (HOPV19) (/HOPV19)
Event date :	from 12-05-2019 to 15-05.2019
Event organizer :	Prashant Kamat, Filippo De Angelis and Aldo Di Carlo
Event place (city) :	Luxembourg
Event country :	Italia
Permalink :	http://hdl.handle.net/10993/47900
Other URL :	https://www.nanoge.org/proceedings/HOPV19/5c35fb65c551940ccd2f4349

File(s) associated to this reference

Fulltext file(s):

	File	Commentary	Version	Size	Access
Limited access	hopv2019.pdf		Author postprint	196.57 kB	Request a copy

All documents in ORBi^lu are protected by a user license.