Understanding machine-learned density functionals

International Journal of Quantum Chemistry

Volumn 116, Issue 11, 2016, Pages 819-833

  Li, Li ^a   Snyder, John C ^b,c   Pelaschier, Isabelle M ^a,d   Huang, Jessica ^a   Niranjan, Uma Naresh ^e   Duncan, Paul ^a   Rupp, Matthias ^f,g   Müller, Klaus Robert ^b,h   Burke, Kieron ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

^c MAX PLANCK INSTITUTE OF MICROSTRUCTURE PHYSICS   (Germany)

^d VANDERBILT UNIVERSITY   (United States)

^e Irvine   (United States)

^f UNIVERSITY OF BASEL   (Switzerland)

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

^h Korea University   (South Korea)

Author keywords

density functional theory; kinetic energy functional; machine learning; orbital free; self consistent calculation

Indexed keywords

ARTIFICIAL INTELLIGENCE; CONSTRAINED OPTIMIZATION; KINETIC ENERGY; KINETICS; LEARNING SYSTEMS; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS; STATISTICAL MECHANICS;

CONSTRAINED MINIMIZATION; DENSITY FUNCTIONAL APPROXIMATIONS; ENERGY FUNCTIONALS; KERNEL RIDGE REGRESSIONS; LOCAL PRINCIPAL COMPONENT ANALYSIS; ORBITAL FREE; SELF-CONSISTENT CALCULATION; SELF-CONSISTENT SOLUTION;

DENSITY FUNCTIONAL THEORY;

EID: 84947256057     PISSN: 00207608     EISSN: 1097461X     Source Type: Journal    
DOI: 10.1002/qua.25040     Document Type: Review

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