Large-scale quantum-accurate atomistic simulation of plasma-facing materials for fusion energy

Plasma-facing materials within fusion reactors are subject to extreme conditions including high particle fluxes of a variety of plasma species and high heat loads.  Developing materials that can handle these extreme conditions is difficult but atomistic modeling like molecular dynamics (MD) can play a key role in fundamental understanding of how these materials degrade in high radiation environments.  However, one of the limitations of MD simulations is the lack of accurate interatomic potentials (IAPs) especially when modeling materials in extreme environments like at the plasma-material interface.  New machine learned interatomic potentials like the Spectral Neighbor Analysis Potential (SNAP) have been shown to have a higher accuracy compared to classical potentials, allowing for quantum accuracy with MD scalability.  We have developed a series of SNAP potentials for studying plasma-material interactions in W, W-ZrC, and MoNbTaTi.  SNAPs optimization on exascale machines allows for unprecedented extremely large, highly accurate MD simulations.  In this presentation, we will discuss the development of SNAP interatomic potentials for modeling plasma-material interactions and subsequent simulations of large-scale MD simulations investigating the effect of plasma exposure to candidate plasma-facing materials using leadership class computing resources.  SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.