Data-Driven Combinatorial Design of Highly Energetic Materials

Abstract

In this Account, we present a comprehensive overview of recent advancements in applying data-driven combinatorial design for developing novel high-energy-density materials. Initially, we outline the progress in energetic materials (EMs) development within the framework of the four scientific paradigms, with particular emphasis on the opportunities afforded by the evolution of computer and data science, which has propelled the theoretical design of EMs into a new era of data-driven development. We then discuss the structural features of typical EMs such as TNT, RDX, HMX, and CL-20, namely, a “scaffolds + functional groups” characteristic, underscoring the efficacy of the combinatorial design approach in constructing novel EMs. It has been discerned that those modifications to the scaffolds are the primary driving force behind the enhancement of EMs’ properties.