Enthalpy of Formation Prediction for Energetic Materials Based on Deep Learning

In order to speed up the development of new energetic materials and reduce the time and resource consumption caused by a large number of experiments, a method for predicting enthalpy of formation of energetic materials is proposed based on the theory of material genetic engineering. Firstly, the collected atomic coordinate data representing the molecular structure of energetic materials were converted into a coulomb matrix representing the cartesian coordinate system in the molecule to eliminate the influence of translation, rotation, index order and other operations on the prediction of enthalpy of formation. Then, the enthalpy of formation of energetic materials was predicted according to the proposed fusion model of Convolutional Neural Network (CNN) and Bi-directional Long Short-term Memory Network (Bi-LSTM) based on Attention mechanism. In this way, not only can the characteristics of the data be extracted effectively, but also the correlation between the data and the lack of long-term dependence can be fully considered. Meanwhile, the influence of important characteristics on the prediction results can be highlighted. The comparison of experimental results shows that the proposed method based on deep learning has the lowest experimental error in the prediction of enthalpy of formation. Its Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and Root Mean Squared Logarithmic Error (RMSLE) are 0.0374, 1.32%, 0.0541 and 0.028, respectively. The prediction goal of "structure-performance" is realized, and a new method is provided for the prediction of enthalpy of formation of energetic materials. © 2021, Editorial Board of Chinese Journal of Energetic Materials. All right reserved.