Relationships with oxygen balance and bond dissociation energies

Determining the mechanical and decomposition properties of high energetic materials (α-RDX, β-HMX, and ε-CL-20) using a neural network potential

“…[1][2][3] They can rapidly react and release immense energy in a very short period, supporting a wide range of practical applications such as propellants, explosives, and pyrotechnics. 2,4,5 Among the most widely used high-energy materials in ammunition and solid propellants are 1,3,5-trinitroperhydro-1,3,5-triazine (C 3 H 6 N 6 O 6 , RDX), hexahydro-1,3,5-trinitro-1,3,5-triazine (C 4 H 8 N 8 O 8 , HMX), and 2,4,6,8,10,12-hexanitrohexaazaisowurtzitane (C 6 H 6 N 12 O 12 , CL-20), 6,7 due to their unique structures, and the outstanding performance. The combustion/detonation characteristics, and mechanical stability of the HEMs are crucial for the practical application.…”

Impact sensitivities of energetic materials derived from easy-to-compute ab initio rate constants

“…5 This has stimulated many efforts devoted to develop predictive models for impact sensitivity. Following early correlations linking h 50 to molecular properties, including oxygen balance, [6][7][8] charge distributions 9 or bond dissociation energies, 10,11 quantitative structure-property relationship (QSPR) approaches led to a growing number of statistical or machine learning (ML) models for h 50 , as reviewed recently by Rice and Byrd. 12 However, notwithstanding a recent study based on a very large database and carefully selected descriptors, 13 such models are usually restricted in scope to compounds similar to those used for their parametrization, due to their lack of physical grounds combined with the relative scarcity of impact sensitivity measurements.…”