Machine-learned interatomic potential for sputtering of tungsten-boron surfaces

Abstract

Boronization, where boron is deposited onto tungsten surfaces, is a key technique to reduce plasma contamination, such as oxygen in Tokamak fusion reactors. The exact interaction between the boron atoms and the tungsten surface, and the effect of the harsh environment on these surfaces are however not fully understood, partially due to the lack of accurate atomistic simulations and interatomic potentials. Here, we develop a machine-learned interatomic potential for sputtering studies of W-B structures and deposition of boron onto tungsten surfaces. The machine-learned potential is trained to density functional theory data and allows accurate large-scale molecular dynamics simulations. Our aim is to understand how boron behaves when deposited on tungsten and how tungsten and boron are sputtered under irradiation. We observe that both the surface configuration/orientation and the surface composition affect the sputtering, and that depositing boron onto tungsten surfaces produces a dense boron layer. The developed potential shows good accuracy for both surface and bulk properties and can be used for simulations of mixed W and B systems.