Application of Machine Learning to the Design of Energetic Materials: Preliminary Experience and Comparison with Alternative Techniques

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.