Comparing the Catalytic Effect of Metals for Energetic Materials: Machine Learning Prediction of Adsorption Energies on Metals

Energetic materials (EMs) and metals are the important components of solid propellants, and a strong catalysis of metals on EMs could further enhance the combustion performance of solid propellants. Accordingly, the study on the adsorption of EMs such as octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX), hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX), and ammonium dinitramide (ADN) on metals (Ti, Zr, Fe, Ni, Cu, and Al) was carried out by density functional theory (DFT) to reveal the catalytic effect of metals. The deep dissociation of EMs on Ti and Zr represents a stronger interaction and corresponds to the rapid thermal decomposition behavior of the EMs/metal composite in the experiment. It is expected that DFT calculation can be selected instead of experiments to compare the catalytic effect of metals and preliminarily screen out potential high-performance metals. Based on the data set of the calculated adsorption energy, further machine learning (ML) was used to predict the adsorption energy of EMs on metals for a convenient comparison of the catalytic effect of metals, since a quite high adsorption energy value represents a more thorough dissociation. The kernel ridge regression (KRR) method shows the best performance on predicting adsorption energy and helps to choose the metals for efficiently catalyzing ammonium nitrate (AN) and hexanitrohexaazaisowurtzitane (CL-20). Such adsorption computation and ML not only reveal the decomposition mechanism of EMs on metals but also provide a simple underlying method to predict the catalytic effect of metals.