First principles reactive simulation for equation of state prediction

TL;DR

This paper introduces a novel ab initio simulation approach combining large-scale computing and data science to evaluate the equation of state for high explosives, revealing limitations of DFT and proposing improved modeling strategies.

Contribution

It presents a new simulation framework for EOS prediction of explosives, highlighting DFT limitations and suggesting methods to incorporate additional molecular species.

Findings

DFT systematically overestimates PETN detonation product energies

Underprediction of detonation velocity, pressure, and temperature by DFT

Modeling strategy suggests new molecular species for thermochemical analysis

Abstract

The high cost of density functional theory has hitherto limited the ab initio prediction of equation of state (EOS). In this article, we employ a combination of large scale computing, advanced simulation techniques, and smart data science strategies to provide an unprecedented, ab initio performance analysis of the high explosive pentaerythritol tetranitrate (PETN). Comparison to both experiment and thermochemical predictions reveals important quantitative limitations of DFT for EOS prediction, and thus the assessment of high explosives. In particular, we find DFT predicts the energy of PETN detonation products to be systematically too high relative to the unreacted neat crystalline material, resulting in an underprediction of the detonation velocity, pressure, and temperature at the Chapman-Jouguet (CJ) state. The energetic bias can be partially accounted for by high-level electronic structure calculations of the product molecules. We also demonstrate a modeling strategy for mapping chemical composition across a wide parameter space with limited numerical data, the results of which suggest additional molecular species to consider in thermochemical modeling.