Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity

Journal of Chemical Physics

Volumn 145, Issue 16, 2016, Pages

  Huang, Bing ^a   Von Lilienfeld, O Anatole ^a  

^a UNIVERSITY OF BASEL   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CHEMICAL BONDS; ELECTRON AFFINITY; ELECTRONIC PROPERTIES; IONIZATION POTENTIAL; LEARNING SYSTEMS; MOLECULES; POTENTIAL ENERGY; QUANTUM THEORY; SPECIFIC HEAT;

ELECTRONIC EXCITATION; MANY-BODY EXPANSION; MOLECULAR PROPERTIES; MOLECULAR REPRESENTATIONS; SIMILARITY CRITERIA; SMALL ORGANIC MOLECULES; UNIVERSAL FORCE FIELDS; ZERO-POINT VIBRATIONAL ENERGIES;

DENSITY FUNCTIONAL THEORY;

EID: 84994037361     PISSN: 00219606     EISSN: None     Source Type: Journal    
DOI: 10.1063/1.4964627     Document Type: Article

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