Machine Learned Interatomic Potential Development for W-ZrC

While tungsten is a leading candidate for the divertor material in future fusion reactors, its performance is limited by its thermo-mechanical properties, such as high ductile to brittle transition temperature and substantial recrystallization at ITER operating conditions. To improve mechanical performance, dispersoids like zirconium carbide can be added during manufacturing. Molecular dynamics (MD) can be leveraged to better understand how such microstructural changes will impact divertor material performance. However, there is a lack of accurate W-ZrC interatomic potentials. In this work, we will describe a machine learned Spectral Neighbor Analysis Potential (SNAP) developed for the W-ZrC system. The SNAP potential is trained on Density Functional Theory data for the respective pure materials, surfaces, and W-ZrC interfaces. Ab initio Molecular Dynamics data is additionally included in the training set to improve applicability of the potential to simulations at temperature. We will discuss the use and accuracy of the newly developed W-ZrC potential in running MD simulations of ZrC dispersoids in W.