Deep Learning for Energetic Materials: Predicting Material Properties from Electronic Structure using Convolutional Neural Networks

Developing numerical descriptions of complex objects, like molecular structure, is a difficult task. 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. 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. We develop a convolution neural network capable of directly parsing the 3D electronic structure of a molecule described by spatial point data for charge density and electrostatic potential concatenated into a 4D tensor. The 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.