Molecular properties of energetic materials and the use of quantumchemical methods for their determination

Energetic materials find countless applications of relevance to the defence sector, as typical classes of energetic materials are propellants, explosives and fuels. To ensure safe handling of such materials, obtaining knowledge about their sensitivity to impact is crucial. In this report, quantum chemical methods are utilized to consider the problem of sensitivity from different angles. Such computational methods might complement – and even replace – practical experi-ments in some cases. Included topics are determination of transition states, machine learning as a quantum chemistry tool, and the desensitizing effect of amino groups in nitro-containing explosives. For the latter, two ethene derivatives as well as their anionic counterparts are explored and compared. The Gaussian 09 software is employed for density functional theory (DFT) calculations, and the parameters of interest are the dissociation energies of different C–NO2 cleavage reactions, bond lengths, and partial charges. The results point towards Z-1-amino-2-nitroethene being less sensitive than nitroethene, due to increased conjugation by amino electron donation and perhaps increased hydrogen bonding. From the computational results related to the anions, detonation is expected to occur at lower temperatures in the cases where electric sparks may occur. The transition state geometry for nitrobenzene proposed in the literature was confirmed through a frequency calculation. Neural networks and SchNetPack (a deep learning toolbox for atomistic systems) are topics of discussion, but further work is needed in order to produce useful results.