The last few years have seen a steep rise in the use of data-driven methods in different scientific fields historically relying on theoretical or empirical approaches. Chemistry is at the forefront of this paradigm shift due to the longstanding use of computational tools involved in the calculation of molecular structures and properties. In this paper, we showcase examples from the literature as well as work in progress in our lab in order to give a brief overview on how these methods can benefit the energetic materials community. A deep learning approach is compared to "traditional" QSPR and semi-empirical approaches for molecular property prediction, and specificities inherent to energetic materials are discussed. Deep generative models for the design of new energetic materials are also presented. We conclude by giving our view on the most promising strategies for future in silico generation of new energetic materials satisfying the performance/sensitivity trade-off.

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Confinement effects on the ro-translational (RT) dynamics of water, trapped in rare gas matrices or within endofullerenes (i.e., H2O@C60), can be experimentally assessed using rotationally resolved far-infrared, or mid-infrared, spectroscopy [Putaud et al., J. Chem. Phys. 156, 074305 (2022) (Paper II)]. The confined rotor model is used here to reveal how the quantized rotational and frustrated translational energy levels of confined water interact and mix by way of the confinement-induced rotation-translation coupling (RTC). An eccentric but otherwise isotropic 3D harmonic effective potential is used to account for confinement effects, thereby allowing the dependence of the magnitude of the RTC on the topology of the model confinement potential, the resulting intricate mixing schemes, and their impact on the RT energy levels to be examined in detail. The confined rotor model thus provides a convenient framework to investigate the matrix and isotope effects on the RT dynamics of water under extreme confinement probed spectroscopically, thereby potentially providing insight into the mechanisms and rates for ortho-H2O ↔ para-H2O nuclear spin isomer interconversion in confined water.

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Water molecules trapped in rare gas matrices exhibit conspicuous shifts in their far-infrared (FIR), rotranslational spectral features compared with the corresponding transitions observed in the gas phase. These confinement-induced perturbations have been related not only to the quantization of translational motion but also to the coupling between the orientational and positional degrees of freedom: the rotation–translation coupling (RTC). As the propensity displayed by the nuclear spin isomers (NSI) of water to undergo interconversion in confinement is intimately related to how its nuclear spin degrees of freedom are coupled with those for intra- and intermolecular motions, confinement-induced RTC should also strongly impact the NSI interconversion mechanisms and rates. Insight into the rotranslational dynamics for H216O, H217O, and H218O, confined in argon and krypton matrices, is provided here based on the evolution of rotranslational spectra induced by NSI interconversion while a definitive assignment is provided from the transition energies and intensities calculated using the confined rotor model [Paper I, Wespiser et al., J. Chem. Phys. 156, 074304 (2021)]. In order to build a complete rotranslational energy diagram of confined water, which is fundamental to understand the NSI interconversion rates, the energy difference between the ground ortho and para rotranslational states is derived from the temperature dependence of the intensity ratio of mid-infrared lines emerging from these states. These investigations should provide deeper insight of the factors that control NSI interconversion of water isotopologues under extreme confinement.