Prediction of Energetic Material Properties from Electronic Structure Using 3D Convolutional Neural Networks

We
develop a convolutional neural network capable of directly parsing
the 3D electronic structure of a molecule described by spatial point
data for charge density and electrostatic potential represented as
a 4D tensor. This method effectively bypasses the need to construct
complex representations, or descriptors, of a molecule. This is beneficial
because the accuracy of a machine learned model depends on the input
representation. Ideally, input descriptors encode the essential physics
and chemistry that influence the target property. Thousands of molecular
descriptors have been proposed, and proper selection of features requires
considerable domain expertise or exhaustive and careful statistical
downselection. In contrast, deep learning networks are capable of
learning rich data representations. This provides a compelling motivation
to use deep learning networks to learn molecular structure–property
relations from “raw” data. The convolutional neural
network model is jointly trained on over 20,000 molecules that are
potentially energetic materials (explosives) to predict dipole moment,
total electronic energy, Chapman–Jouguet (C–J) detonation
velocity, C–J pressure, C–J temperature, crystal density,
HOMO–LUMO gap, and solid phase heat of formation. This work
demonstrates the first use of complete 3D electronic structure for
machine learning of molecular properties.