  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 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 suppression 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.
