Machine learning approaches for predicting impact sensitivity and detonation performances of energetic materials

Excellent detonation performances and low sensitivity are prerequisites for the deployment of energetic materials. Exploring the underlying factors that affect impact sensitivity and detonation performances as well as exploring how to obtain materials with desired properties remains a long-term challenge. Machine learning with its ability to solve complex tasks and perform robust data processing can reveal the relationship between performance and descriptive indicators, potentially accelerating the development process of energetic materials. In this background, impact sensitivity, detonation performances, and 28 physicochemical parameters for 222 energetic materials from density functional theory calculations and published literature were sorted out. Four machine learning algorithms were employed to predict various properties of energetic materials, including impact sensitivity, detonation velocity, detonation pressure, and Gurney energy. Analysis of Pearson coefficients and feature importance showed that the heat of explosion, oxygen balance, decomposition products, and HOMO energy levels have a strong correlation with the impact sensitivity of energetic materials. Oxygen balance, decomposition products, and density have a strong correlation with detonation performances. Utilizing impact sensitivity of 2,3,4-trinitrotoluene and the detonation performances of 2,4,6-trinitrobenzene-1,3,5-triamine as the benchmark, the analysis of feature importance rankings and statistical data revealed the optimal range of key features balancing impact sensitivity and detonation performances: oxygen balance values should be between 40% and 30%, density should range from 1.66 to 1.72 g/cm3, HOMO energy levels should be between 6.34 and 6.31 eV, and lipophilicity should be between 1.0 and 0.1, 4.49 and 5.59. These findings not only offer important insights into the impact sensitivity and detonation performances of energetic materials, but also provide a theoretical guidance paradigm for the design and development of new energetic materials with optimal detonation performances and reduced sensitivity. (c) 2024 Science Press and Dalian Institute of Chemical Physics, Chinese Academy of Sciences. Published by Elsevier B.V. and Science Press. All rights are reserved, including those for text and data mining, AI training, and similar technologies.