Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space

Journal of Physical Chemistry Letters

Volumn 6, Issue 12, 2015, Pages 2326-2331

  Hansen, Katja ^a   Biegler, Franziska ^b   Ramakrishnan, Raghunathan ^c   Pronobis, Wiktor ^b   Von Lilienfeld, O Anatole ^c,d   Müller, Klaus Robert ^b,e   Tkatchenko, Alexandre ^a  

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

^b TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

^c UNIVERSITY OF BASEL   (Switzerland)

^d ARGONNE NATIONAL LABORATORY   (United States)

^e Korea University   (South Korea)

Author keywords

atomization energies; chemical compound space; machine learning; many body potentials; molecular properties

Indexed keywords

ARTIFICIAL INTELLIGENCE; ATOMIZATION; CHEMICAL COMPOUNDS; DENSITY FUNCTIONAL THEORY; ELECTRONIC PROPERTIES; FORECASTING; LEARNING SYSTEMS; MOLECULES;

ATOMIZATION ENERGIES; FRONTIER ORBITAL ENERGIES; MANY-BODY INTERACTIONS; MANY-BODY POTENTIALS; MOLECULAR PROPERTIES; PHARMACEUTICAL INDUSTRY; RATIONAL COMPOUND DESIGNS; SOPHISTICATED MACHINES;

CHEMICAL BONDS;

ALCOHOL;

CHEMICAL MODEL; CHEMISTRY; MACHINE LEARNING; QUANTUM THEORY; THERMODYNAMICS;

ETHANOL; MACHINE LEARNING; MODELS, CHEMICAL; QUANTUM THEORY; THERMODYNAMICS;

EID: 84935014439     PISSN: None     EISSN: 19487185     Source Type: Journal    
DOI: 10.1021/acs.jpclett.5b00831     Document Type: Article

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