Interpretable Performance Models for Energetic Materials using Parsimonious Neural Networks.

ABSTRACT: Predictive models for the performance of explosives and propellants are important for their design, optimization, and safety. Thermochemical codes can predict some of these properties from fundamental quantities such as density and formation energies that can be obtained from first principles. Models that are simpler to evaluate are desirable for efficient, rapid screening of material screening. In addition, interpretable models can provide insight into the physics and chemistry of these materials that could be useful to direct new synthesis. Current state-of-the-art performance models are based on either the parametrization of physics-based expressions or data-driven approaches with minimal interpretability. We use parsimonious neural networks (PNNs) to discover interpretable models for t

SUBMITTER: Appleton RJ 

PROVIDER: S-EPMC10875666 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature