First-Principles Performance Prediction of High Explosives Enabled by Machine Learning

Abstract

Accurate modeling of the behavior of high-explosive (HE) materials requires knowledge of the equation of state (EOS) for both the reactant and the product states of the material. Historically, EOS models have been calibrated to reproduce experimental data, but there is growing interest in first-principles predictions of HE behavior. The product state is particularly challenging to model because of the wide range of density and temperature conditions that are relevant as well as the requirement to include chemical reactivity in any kind of atomistic simulation. Density functional theory (DFT) simulations are a natural choice for such simulations, but computational cost remains a challenge to the direct application of DFT simulations to HE product EOS development. We recently introduced a machine-learning-driven methodology to address these challenges that was successfully applied to a single type of HE (penta-erythritoltetranitrate, or PETN), but there were several open questions about the generality of the approach. In particular, we had to develop an approximate scheme to correct the DFT energies of the product state using the energy differences between coupled cluster theory and DFT calculations for relevant molecular species in the gas phase to achieve good agreement with experiment. In this work, we apply the method to two additional HEs (octogen and 3,3′-diamino-4,4′-azoxyfurazan) to address these outstanding questions. In this work, we again find deficiencies in the DFT energetic description of the product state. However, we also find that the previously proposed procedure works well to correct the DFT energies for these other HEs, increasing confidence in the method.

Virtual Special Issue on Machine Learning in Physical Chemistry Volume 2

“…In JPC C , the special issue papers describe ML and other data science techniques utilized in the scope of nanoparticles and nanostructures; surface and interface processes; electron, ion, and thermal transport; optic, electronic, and optoelectronic materials; and catalysts and catalysis; as well as energy conversion and storage materials and processes. As in the Parts A and B, a number of articles directly address either the development of new methods or the use of ML methods in new ways. − Several contributions relate to methods in the use or development of so-called machine learning potentials (MLP) or machine learning interaction potentials (MLIP), including a Perspective on improving these models authored by Maxson et al, as well as other contributions. − Interfaces and related phenomena are addressed in several articles. − Nanomaterials and their properties are also prominently featured. − Another large category in JPC C includes studies that address the calculation of properties of materials for wide-ranging applications. − …”