Premium
Neural network for prediction of 13 C NMR chemical shifts of fullerene C 60 mono‐adducts
Author(s) -
Kiryanov Ilya I.,
Tulyabaev Arthur R.,
Mukminov Farit Kh.,
Khalilov Leonard M.
Publication year - 2018
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.3037
Subject(s) - artificial neural network , fullerene , biological system , parametric statistics , artificial intelligence , adduct , chemical shift , computer science , pattern recognition (psychology) , chemistry , mathematics , organic chemistry , statistics , biology
Real‐valued models based on deep artificial neural networks were proposed to predict 13 C NMR chemical shifts of fullerene C 60 core carbon atoms for computer‐aided structure elucidation of complex fullerene C 60 mono‐adducts. We showed that parametric rectified linear units could be successfully used as activation functions in hidden layers of artificial neural networks for decision of complex physical‐chemical tasks. A total of 400 artificial neural networks were trained and tested in order to reveal the best‐fitted models. The best prediction accuracy of real‐valued models was achieved with MAEP = 1.83 ppm/RMSEP = 2.60 ppm using artificial neural network model which has 110 and 120 hidden units, respectively, with parametric rectified linear unit as activation function.