Deep learning model for predicting the spatial distribution of binding energy from atomic configurations

Abstract

Understanding plasma-material interaction is crucial for achieving steady-state operation of magnetic confinement fusion devices. Kinetic Monte Carlo (kMC) simulation is a powerful tool for investigating the motion of atoms in the plasma facing materials under the influence of this interaction. To predict trapping sites and migration energies necessary for kMC simulations, we developed a deep learning model based on pix2pix for predicting the spatial distribution of binding energy. Results show that the model can reproduce spatial distributions similar to the true values. However, larger errors occur in regions with steep value gradients.