First principles reactive simulation for equation of state prediction
arXiv:2103.09849 · doi:10.1063/5.0050676
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.
arXiv comment16 pages, 8 figures
References in corpus (11)
- Separable Dual Space Gaussian Pseudo-potentials
- Relativistic separable dual-space Gaussian Pseudopotentials from H to Rn
- ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
- A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
- Less is more: sampling chemical space with active learning
- Models and algorithms for the next generation of glass transition studies
- Extending the Accuracy of the SNAP Interatomic Potential Form
- New Phases of Water Ice Predicted at Megabar Pressures
- Thermal Density Functional Theory in Context
- Ab Initio Simulations of Hot, Dense Methane During Shock Experiments
- An Efficient Approach to Ab Initio Monte Carlo Simulation