Prediction of impact sensitivity of nitro energetic compounds by neural network based on electrotopological-state indices

Abstract

A quantitative structure–property relationship (QSPR) model was constructed to predict the impact sensitivity of 156 nitro energetic compounds by means of artificial neural network (ANN). Electrotopological-state indices (ETSI) were used as molecular structure descriptors which combined together both electronic and topological characteristics of the analyzed molecules. The typical back-propagation neural network (BPNN) was employed for fitting the possible non-linear relationship existed between the ETSI and impact sensitivity. The dataset of 156 nitro compounds was randomly divided into a training set (64), a validation set (63) and a prediction set (29). The optimal condition of the neural network was obtained by adjusting various parameters by trial-and-error. Simulated with the final optimum BP neural network [16–12–1], the results show that most of the predicted impact sensitivity values are in good agreement with the experimental data, which are superior to those obtained by multiple linear regression (MLR) and partial least squares (PLS). The model proposed can be used not only to reveal the quantitative relation between impact sensitivity and molecular structures of nitro energetic compounds, but also to predict the impact sensitivity of nitro compounds for engineering.