Neural network for prediction of 13 C NMR chemical shifts of fullerene C 60 mono‐adducts

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. A complex set of atomic descriptors for fullerene core carbons is suggested based on modern approaches. Real‐valued 13 C NMR shifts predictor based on neural network is put forward for complex fullerene C 60 mono‐adducts. Parametric rectified linear unit activation function is shown as suitable for 13 C NMR prediction models for complex fullerene C 60 derivatives.