SchNet - A deep learning architecture for molecules and materials

Journal of Chemical Physics

Volumn 148, Issue 24, 2018, Pages

  Schutt K T ^a   Sauceda, H E ^b   Kindermans, P J ^a   Tkatchenko, A ^c   Muller K R ^a,d,e  

^a TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

^b FRITZ HABER INSTITUT DER MAX PLANCK GESELLSCHAFT   (Germany)

^c UNIVERSITY OF LUXEMBOURG   (Luxembourg)

^d MAX PLANCK INSTITUTE FOR INFORMATICS   (Germany)

^e Korea University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CHARACTER RECOGNITION; MOLECULAR DYNAMICS; MOLECULES; POTENTIAL ENERGY; POTENTIAL ENERGY SURFACES; QUANTUM CHEMISTRY; SPEECH RECOGNITION;

AB INITIO MOLECULAR DYNAMICS; ATOMISTIC SYSTEM; CHEMICAL PHYSICS; ENERGY-CONSERVING; LEARNING ARCHITECTURES; MOLECULAR DYNAMICS SIMULATIONS; NONLINEAR POTENTIAL; QUANTUM MECHANICAL;

DEEP LEARNING;

EID: 85044731105     PISSN: 00219606     EISSN: None     Source Type: Journal    
DOI: 10.1063/1.5019779     Document Type: Article

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