Machine Learning Models for High Explosive Crystal Density and Performance

The rate of discovery of new explosives with superior
energy density
and performance has largely stalled. Rapid property prediction through
machine learning has the potential to accelerate the discovery of
new molecules by screening of large numbers of molecules before they
are ever synthesized. To support this goal, we assembled a 21,000-molecule
database of experimentally synthesized molecules containing energetic
functional groups. Using a combination of experimental density measurements
and high throughput electronic structure and atomistic calculations,
we calculated detonation velocities and pressures for all 21,000 compounds.
Using these values, we trained machine learning models for the prediction
of density, detonation velocity and detonation pressure. Notably,
our model for crystal density surpassed the accuracy of all current
models and decreased the root-mean square error (RMSE) of the previous
best model by 20%. This improvement in model performance relative
to past works is attributed to our handling of chiral-specified Simplified
Molecular-Input Line-Entry System (SMILES) strings and introduction
of a new molecular descriptor, MolDensity. To elucidate descriptor
importance, we evaluated interpretable descriptors in terms of importance
and compared the accuracy of a statistics-driven machine learning
model against a model comprised of descriptors typically assumed to
control material density. The inexpensive, yet highly accurate predictions
from our models should enable creation of future artificial intelligence
(AI) models that are able to screen large numbers (>106) of compounds to find the highest performing compounds in terms
of crystal density, detonation velocity and detonation pressure.