Interpretable Performance Models for Energetic Materials using Parsimonious Neural Networks

Predictive models for the performance of explosives and propellants are important for their design, optimization, and safety. Thermochemical codes can predict certain properties from fundamental properties such as density and formation energies that can be obtained from first principles. The Kamlet-Jacobs equations provide a computationally inexpensive alternative, but still require these fundamental properties as inputs and are limited in their ability to generalize to different types of explosives. Such easy to evaluate models are desirable for the efficient screening of large numbers of candidate materials, beyond what is possible with computationally intensive methods. Therefore, we use parsimonious neural networks (PNNs) to learn interpretable models for the detonation velocity and pressure for explosives using data collected from open literature. PNNs use evolutionary optimization to create models that balance accuracy and complexity. For both detonation velocity and pressure, we establish a family of interpretable models that are pareto optimal in accuracy and simplicity space. The Kamlet-Jacobs models lie close to but not at the pareto front. We extract expressions from these models and draw conclusions based on the functional forms of the terms discovered.