Neural network approach to quantum-chemistry data: Accurate prediction of density functional theory energies

Journal of Chemical Physics

Volumn 131, Issue 7, 2009, Pages

  Balabin, Roman M ^a   Lomakina, Ekaterina I ^b  

^a ETH ZURICH   (Switzerland)

^b MOSCOW STATE UNIVERSITY   (Russian Federation)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURATE PREDICTION; ARTIFICIAL NEURAL NETWORK; BASIS SETS; CALIBRATION MODEL; CDS; CROSS VALIDATION; DENSITY FUNCTIONALS; ENERGY ESTIMATION; ENERGY VALUE; HARTREE-FOCK; HIDDEN NEURONS; MEAN ABSOLUTE DEVIATIONS; MOLECULAR DESCRIPTORS; MULTIPLE LINEAR REGRESSIONS; NEURAL NETWORK STRUCTURE OPTIMIZATION; PREDICTION ERRORS; QUANTUM-CHEMISTRY;

BACKPROPAGATION; NEURAL NETWORKS; PROBABILITY DENSITY FUNCTION; SEMICONDUCTOR QUANTUM DOTS; SET THEORY; STRUCTURAL OPTIMIZATION;

DENSITY FUNCTIONAL THEORY;

EID: 69249187434     PISSN: 00219606     EISSN: None     Source Type: Journal    
DOI: 10.1063/1.3206326     Document Type: Article

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57. See EPAPS supplementary material at http://dx.doi.org/10.1063/1.3206326 E-JCPSA6-131-047932 for molecular set used for DFT+ANN study, all DFT (HF, BLYP, B3LYP, BMK) energies with 6-311G, 6-311G (d), 6-311G (d,p), 6-311G (2df,2pd), and 6-311G (3df,3pd) BSs, and ready-to-use MATLAB.mat file with best ANN parameters, presented in table format.