The detonation heat prediction of nitrogen-containing compounds based on quantitative structure-activity relationship (QSAR) combined with random forest (RF)

Abstract The detonation heat is the main performance index of ammunition used in air-to-air and underwater weapons. In the present work, a method for predicting the detonation heat performance of nitrogen-containing compounds based on RF combined QSAR is established. Firstly, the molecular descriptors of nitrogen-containing compounds and the corresponding detonation heat performance parameters are calculated by Dragon 5.4 software, and normalization was used for eliminating orders of difference between the unit and order of magnitude. On the basis, the variable importance (VI) was applied to select the feature variables in the molecular descriptor, and the variable importance threshold was optimized based on out of bag (OOB) error estimation. Then, the optimized feature variables were compression by wavelet transform (WT), and the obtained result was compared with the RF models based on VI. Under the optimized characteristic molecular descriptors, an RF-QSAR relationship model based on the prediction of the detonation heat performance of nitrogen-containing compounds was constructed, and the detonation heat performance of nitrogen-containing compounds in the prediction set was predicted. The results show that the wavelet transform-variable importance-random forest-quantitative structure-activity relationship (WT-VI-RF-QSAR) model can obtain better prediction performance and can more accurately predict the detonation heat performance of nitrogen-containing compounds. The correlation coefficient of calibration set (R2C) is 0.9652, the mean relative error (MREC) is 9.03%, and the R2P of prediction set is 0.8801, MREP is 10.52%. Compared with traditional experimental testing methods, this method has the advantages of fast analysis speed, accurate prediction results, and short monitoring period, etc. This research provides new ideas and methods for the performance prediction of energetic compounds and the design of new energetic materials.