Development of SNAP Interatomic Potentials for Gas-Metal Interactions for Fusion Energy Materials

Tungsten is currently the candidate material for the divertor component of future fusion reactors.  The divertor will be subject to high particle fluxes of a variety of plasma species including both hydrogen and nitrogen.  This results in a variety of microstructural changes including hydrogen blister and tungsten-nitride formation that require further understanding to prevent material degradation.  Modeling like molecular dynamics (MD) can provide insight into material deformation, but accurate interatomic potentials (IAPs) are limited for these types of material interactions especially given the complexity of accurately representing the range of chemical environments for gas-metal interactions.  Machine learned interatomic potentials like the Spectral Neighbor Analysis Potential (SNAP) have been shown to have higher accuracy compared to traditional IAPs and may be well suited to modeling these types of gas-metal interactions.  In this work, the development of W-H and W-N SNAP potentials that reproduce gas species behavior as well as gas impurity behavior on surfaces and in bulk will be discussed.  MD simulations of hydrogen or nitrogen implantation in tungsten will be shown.  SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.