AI-enhanced quantum simulations of reactive structural energetic materials using probabilistic combinatorial optimization under thermomechanical compression

Reactive structural energetic materials can react to generate energy though their reaction to release energy may be slower than the deflagration or detonation of conventional explosives. Typically, reactive structural energetic materials are solid mixtures such as thermites, intermetallic alloys, and metal oxides. The reaction rate may vary over a relatively wide range, depending on the chemical composition and structures of the contact area between reacting chemical species. Therefore, the reaction rate is one of the most important properties used to determine the application of weapons systems. Using probabilistic combinatorial optimization as a statistical method and machine learning, we developed quantum simulations of pressure-temperature phase diagrams of reactive structural energetic materials to capture thermomechanical response properties. Machine learning is used for studying the intermetallic bonding space of reactive structural energetic materials and predicting the relevant enthalpy of combustion. The simulation also includes non-perturbation electron-phonon interaction calculations applied to determine initiation reaction modes under thermomechanical compression. The paper reports simulation results of reactive structural intermetallic alloys and metal oxides.