Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields

Reference : Molecular force fields with gradient-domain machine learning (GDML): Comparison and s...


	Document type :	Scientific journals : Article
	Discipline(s) :	Physical, chemical, mathematical & earth Sciences : Physics
	Focus Areas :	Physics and Materials Science
	To cite this reference:	http://hdl.handle.net/10993/45254

Title :	Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields
Language :	English
Author, co-author :	Sauceda, Huziel E []
	Gastegger, Michael []
	Chmiela, Stefan []
	Müller, Klaus-Robert []
	Tkatchenko, Alexandre [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Physics and Materials Science (DPHYMS) >]
Publication date :	2020
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 :	NY
Permalink :	http://hdl.handle.net/10993/45254

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	File	Commentary	Version	Size	Access
Open access	J.Chem.Phys.153_124109_2020.pdf		Publisher postprint	9.1 MB	View/Open

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