Machine learning of accurate energy-conserving molecular force fields

Science Advances

Volumn 3, Issue 5, 2017, Pages

  Chmiela, Stefan ^a   Tkatchenko, Alexandre ^b,c   Sauceda, Huziel E ^c   Poltavsky, Igor ^b   Schütt, Kristof T ^a   Müller, Klaus Robert ^a,d,e  

^a TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

^b UNIVERSITY OF LUXEMBOURG   (Luxembourg)

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

^d Korea University   (South Korea)

^e MAX PLANCK INSTITUTE FOR INFORMATICS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CALCULATIONS; LEARNING SYSTEMS; MOLECULAR DYNAMICS; MOLECULES; NAPHTHALENE; POTENTIAL ENERGY; QUANTUM CHEMISTRY; QUANTUM THEORY; VECTOR SPACES; VECTORS;

AB INITIO MOLECULAR DYNAMICS; CONSERVATION OF ENERGY; FUNDAMENTAL PROPERTIES; GLOBAL POTENTIAL ENERGY SURFACES; LAW OF ENERGY CONSERVATION; MOLECULAR DYNAMICS SIMULATIONS; QUANTUM-MECHANICAL SYSTEM; VECTOR-VALUED FUNCTION;

ENERGY CONSERVATION;

EID: 85041381183     PISSN: None     EISSN: 23752548     Source Type: Journal    
DOI: 10.1126/sciadv.1603015     Document Type: Article

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