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 <i>in silico</i> 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.