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.