Atomistic Modeling of Beryllium and Helium Implantation in Tungsten Using Machine Learned Interatomic Potentials

Tungsten and beryllium have been chosen for the divertor and first wall, respectively, of future tokamaks like ITER due to their favorable material properties [1].  However, the divertor will be subject to high fluxes of a variety of plasma species, including eroded beryllium, which can lead to microstructural changes of the tungsten surface.  In particular, beryllium implantation in tungsten has been experimentally observed to form stable W-Be intermetallics [2].  These intermetallics have a significantly lower melting temperature than pure tungsten, making the divertor more susceptible to melting.  Mixed W-Be layers have also been shown to affect both hydrogen retention [3] and helium fuzz growth [2].  Fundamental understanding of how W-Be intermetallics form and affect the interaction of other plasma species in tungsten is critical.

 

Atomistic modeling can play a key role in discovering mechanisms of material degradation due to plasma exposure. However, accurate interatomic potentials are lacking for these types of materials.  In this work, we will describe the development of a machine learned interatomic potential for studying beryllium implantation in tungsten using molecular dynamics.  Simulations of beryllium implantation in tungsten and the effect of mixed W-Be surface layers on helium bubble nucleation will also be discussed.  Results indicate beryllium implantation in tungsten leads to a near-surface mixed materials layer that can alter helium retention, depth distribution, and bubble size.

 

[1] V. Phillips, J. Nucl. Mater. 415, S2-S9, (2011)

[2] M.J. Baldwin, et al. J. Nucl. Mater. 390-391, 886-890, (2009)

[3] R.P. Doerner, et al. J. Nucl. Mater. 415, S717-S720, (2011)

 

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