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In this paper, twelve 1,3-dinitrohexahydropyrimidine-based energetic compounds were designed by introducing various explosopheres into hexahydropyrimidine skeleton. Their geometric and electronic structures, heats of formation (HOFs), energetic performance, thermal stability and impact sensitivity were discussed. It is found that the incorporation of electron-withdrawing groups (–NO2, –NHNO2, –N3, –CH(NO2)2, –CF(NO2)2, –C(NO2)3) improves HOFs of the derivatives and all the substituents contribute to enhancing the densities and detonation properties (D, P) of the title compounds. Therein, the substitution of –C(NO2)3 features the best energetic performance with detonation velocity of 9.40 km s−1 and detonation pressure of 40.20 GPa. An analysis of the bond dissociation energies suggests that N–NO2 bond may be the initial site in the thermal decompositions for most of the derivatives. Besides, –ONO2 and –NF2 derivatives stand out with lower impact sensitivity. Characters with striking detonation properties (D = 8.62 km s−1, P = 35.08 GPa; D = 8.81 km s−1, P = 34.88 GPa), good thermal stability, and acceptable impact sensitivity (characteristic height H50 over 34 cm) lead novel compounds 5,5-difluoramine-1,3-dinitrohexahydropyrimidine (K) and 5-fluoro-1,3,5-trinitrohexahydropyrimidine (L) to be very promising energetic materials. This work provides the theoretical molecular design and a reasonable synthetic route of L for further experimental synthesis and testing. [ABSTRACT FROM AUTHOR]

• Predicting thermal decomposition temperatures with error of 41–29 K. • Electronic and topological descriptors dominate prediction accuracy. • Descriptors related to multi-level interactions are needed in the future. Machine learning is an emerging approach to predict thermal decomposition temperature in the field of energetic materials, while an assessment of the descriptor applicability is still lacking. In this work, we have systematically established 5 general descriptor sets for 1091 compounds and combined them with 9 algorithms to construct a suite of predictive models with mean absolute error ranging 41–29 K, which is comparable to the cutting-edge endeavors. Our study emphasizes the significant influence of multi-level structural interactions on the thermal stability and decomposition of energetic materials, contributing insights conducive to the development of corresponding descriptors. [Display omitted] [ABSTRACT FROM AUTHOR]