Multiscale reactive model for 1,3,5-triamino-2,4,6-trinitrobenzene inferred by reactive MD simulations and unsupervised learning

When high-energy-density materials are subjected to thermal or mechanical insults at extreme conditions (shock loading), a coupled response between the thermo-mechanical and chemical behavior is systematically induced. We develop a reaction model for the fast chemistry of 1,3,5-triamino-2,4,6- trinitrobenzene (TATB) at the mesoscopic scale where the chemical behavior is determined by underly- ing microscopic reactive simulations. The slow carbon clusters formation is not discussed in the present work. All-atom reactive MD simulations are performed with the ReaxFF potential and a reduced-order chemical kinetics model for TATB is fitted on isothermal and adiabatic simulations of single crystal chemical decomposition. Unsupervised machine learning techniques based on the non-negative matrix factorization are applied to MD trajectories to model the decomposition kinetics of TATB in terms of a four components model. The associated heats of reaction are fit to the temperature evolution from adiabatic decomposition trajectories. Using a chemical species analysis, we show that NMF captures the main chemical decomposition steps of TATB, and provides an accurate estimation of their evolution with temperature. The final analytical formulation, coupled to a diffusion term, is incorporated into a continuum formalism and simulation results are compared one-to-one against MD simulations of a 1D reaction propagation along different crystallographic directions and with different initial temperatures. A good agreement is found for both temporal and spatial evolution of the temperature field.