Nonlinear gradient denoising: Finding accurate extrema from inaccurate functional derivatives

International Journal of Quantum Chemistry

Volumn 115, Issue 16, 2015, Pages 1102-1114

  Snyder, John C ^a,b   Rupp, Matthias ^c   Müller, Klaus Robert ^a,d   Burke, Kieron ^e,f  

^a TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

^b MAX PLANCK INSTITUTE OF MICROSTRUCTURE PHYSICS   (Germany)

^c UNIVERSITY OF BASEL   (Switzerland)

^d Korea University   (South Korea)

^e UNIVERSITY OF CALIFORNIA   (United States)

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

Author keywords

density functional theory; machine learning; nonlinear gradient denoising; orbital free density functional theory

Indexed keywords

GRADIENT METHODS; KINETIC ENERGY; KINETICS; LEARNING SYSTEMS; MACHINE LEARNING; NONLINEAR PROGRAMMING;

DE-NOISING; FUNCTIONAL DERIVATIVES; HIGH-DIMENSIONAL; KINETIC ENERGY DENSITY; NON-LINEAR OPTIMIZATION; NONLINEAR KERNELS; ORBITAL-FREE DENSITY FUNCTIONAL THEORY; PROJECTED GRADIENT;

DENSITY FUNCTIONAL THEORY;

EID: 84936751541     PISSN: 00207608     EISSN: 1097461X     Source Type: Journal    
DOI: 10.1002/qua.24937     Document Type: Article

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