Toward a detailed description of shockwave-driven carbon condensation: Insights from Machine-Learning-Accelerated Simulations

It is well known that carbon rich organic precursors can form large quantities of carbon nanoparticles (CNP) when subject to shock compression or detonation. However, the complicated underlying phenomena remain poorly understood due to spatiotemporal scales that make experimental characterization exceedingly difficult and necesitates "quantum accurate" methods to properly model. Several years ago we demonstrated that our machine-learning ChIMES framework could enable “quantum accurate” atomistic simulations of the reaction-mediated phenomena governing CNP formation in the shocked state on experimental scales, but limitations in our model precluded examining how phase transformation occurs during post-shock expansion and quench. In this talk, we present new insights into this highly non-equilibrium phenomenon enabled by recent advances in our ChIMES framework and models. Implications for interpretation of detonation and laser-shock experiments will be discussed.



This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-ABS-2002307