An approach to predict the 13C NMR chemical shifts of acrylonitrile copolymers using artificial neural network

Abstract

Artificial neural network has been utilized to simulate the 13C{ 1H} NMR chemical shifts for the hydrogen terminated fragments of acrylonitrile copolymers and comparison was done with carbon-13 chemical shift values predicted by partial least square regression analysis (PLSR). In this work, structural descriptors were linked to the chemical shift values applying back-propagation learning algorithm as well as PLSR. The descriptors used offered a very useful formal tool for the proper and adequate description of environment of carbon atoms in the copolymers. It has been demonstrated that the performance of 13C{ 1H} NMR chemical shift prediction could be made easy using principal component analysis. 13C{ 1H} chemical shift values of methine and methylene carbon atoms of acrylonitrile/butyl methacrylate and acrylonitrile/ethyl acrylate copolymers were predicted with the average mean absolute error of various carbons varies between 0.4 and 1.4 ppm. The calculated chemical shift values have good correlation with the experimental values. The results were compared with partial least square regression method, which afforded the error between 2.0 and 5.5 ppm.