Article (Scientific journals)
Efficient interatomic descriptors for accurate machine learning force fields of extended molecules
TKATCHENKO, Alexandre; KABYLDA, Adil; POLTAVSKYI, Igor et al.
2023In Nature Communications
Peer Reviewed verified by ORBi
 

Files


Full Text
s41467-023-39214-w.pdf
Publisher postprint (2.85 MB)
Download

All documents in ORBilu are protected by a user license.

Send to



Details



Research center :
ULHPC - University of Luxembourg: High Performance Computing
Disciplines :
Physics
Author, co-author :
TKATCHENKO, Alexandre ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Physics and Materials Science (DPHYMS)
KABYLDA, Adil ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Physics and Materials Science (DPHYMS)
POLTAVSKYI, Igor ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Physics and Materials Science (DPHYMS)
Vassilev-Galindo Valentin
Chmiela Stefan
External co-authors :
yes
Language :
English
Title :
Efficient interatomic descriptors for accurate machine learning force fields of extended molecules
Publication date :
15 June 2023
Journal title :
Nature Communications
eISSN :
2041-1723
Publisher :
Nature Publishing Group, London, United Kingdom
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBilu :
since 04 August 2023

Statistics


Number of views
60 (8 by Unilu)
Number of downloads
27 (5 by Unilu)

Bibliography


Similar publications



Contact ORBilu