Dimensionality reduction in machine learning for nonadiabatic molecular dynamics: Effectiveness of elemental sublattices in lead halide perovskites

Reference : Dimensionality reduction in machine learning for nonadiabatic molecular dynamics: Eff...


	Document type :	Scientific journals : Article
	Discipline(s) :	Physical, chemical, mathematical & earth Sciences : Physics
	To cite this reference:	http://hdl.handle.net/10993/53243

Title :	Dimensionality reduction in machine learning for nonadiabatic molecular dynamics: Effectiveness of elemental sublattices in lead halide perovskites
Language :	English
Author, co-author :	Tkatchenko, Alexandre [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Physics and Materials Science (DPHYMS) >]
	Wei Bin How []
	Bipeng Wang []
	Weibin Chu []
	Sergiy M. Kovalenko []
	Oleg V. Prezhdo []
Publication date :	2-Feb-2022
Journal title :	Journal of Chemical Physics
Publisher :	American Institute of Physics
Peer reviewed :	Yes
ISSN :	0021-9606
e-ISSN :	1089-7690
City :	New York
Country :	United States - New York
Permalink :	http://hdl.handle.net/10993/53243
DOI :	10.1063/5.0078473

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Open access	J.Chem.Phys.156_054110_2022.pdf		Publisher postprint	7.44 MB	View/Open

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