posted on 2024-02-28, 08:04authored byBeth A. Lindquist, Ryan B. Jadrich, Jeffery A. Leiding
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-erythritol-tetranitrate, 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.