SCOPUS 정보 검색 플랫폼

Magnetic Resonance in Chemistry

Volumn 48, Issue 3, 2010, Pages 219-229

Empirical and DFT GIAO quantum-mechanical methods of 13C chemical shifts prediction: Competitors or collaborators?

(5) Elyashberg, Mikhail a Blinov, Kirill a Smurnyy, Yegor a Churanova, Tatiana a Williams, Antony b

a Moscow Department (Russian Federation)

b ROYAL SOCIETY OF CHEMISTRY (United Kingdom)

Author keywords

Chemical shift prediction; DFT; GIAO; Hose code; Neural nets; NMR

Indexed keywords

CHEMICAL SHIFT; HOSE; QUANTUM THEORY;

CHEMICAL SHIFT PREDICTION; DFT; DFT/GIAO; EMPIRICAL METHOD; GIAO; HOSE CODE; MEAN ABSOLUTE ERROR; NEURAL NETWORKS ALGORITHMS; QUANTUM MECHANICAL; QUANTUM MECHANICAL METHOD;

FORECASTING;

EID: 77349109546 PISSN: 07491581 EISSN: 1097458X Source Type: Journal
DOI: 10.1002/mrc.2571 Document Type: Article

Times cited : (36)

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