  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.
