Machine Learning Densities, Detonation Velocities, and Formation Enthalpies of Energetic Materials Using Quantum Chemistry Descriptors

The prediction of detonation parameters is a challenging task requiring to bridge the gap between microscopic molecular features and macroscopic materials properties. Whereas traditional routes are concerned with empirical equations, we present a machine learning approach to this task here. Our approach capitalizes on molecular descriptors from high-level quantum chemistry as input to produce models fitted against experimental reference data to model three key quantities: the crystalline density, the detonation velocity, and the heat of formation. To determine the detonation products, we use a new nonempirical product optimization scheme, maximizing the heat release, which is extensible to any molecular composition. We find, for all three properties, that the machine-learned results significantly surpass standard rule-based schemes. Finally, we present an all in silico scheme for predicting detonation velocities, highlighting that this is almost as good as when experimental densities are used as input. In summary, we believe that this work is a major step toward the goal of accurately predicting detonation parameters by showing how to leverage the power of quantum chemistry for this task.