Sputtering of the beryllium tungsten alloy Be2W by deuterium atoms: molecular dynamics simulations using machine learned forces. (10th December 2020)

• Record Type: Journal Article
• Authors: Chen, L.; Kaiser, A.; Probst, M.; Shermukhamedov, S.
• Is Part Of: Nuclear fusion. Volume 61:Number 1(2021)
• Publication Date: 2020-12-10
• Subjects: surface sputtering -- machine learning -- molecular dynamics -- plasma wall interaction -- ITER; Nuclear fusion -- Periodicals; 621.48405
• DOI: 10.1088/1741-4326/abc9f4
• Languages: English
• ISSNs: 0029-5515

Abstract

Abstract: Material erosion and fuel retention will limit the life and the performance of thermonuclear fusion reactors. In this work, sputtering, reflection and retention processes are atomistically modeled by simulating the non-cumulative sputtering by deuterium projectiles on a beryllium–tungsten alloy surface. The forces for the molecular dynamics trajectories were machine learned from density functional theory with a neural network architecture. Our data confirms and supplements previous results for simulated sputtering rates. In the non-cumulative scenario we simulate, we did not observe reaction mechanisms leading to swift chemical sputtering. Thus, our sputtering rates at low impact energies are smaller than in comparable non-cumulative studies. The sputtering yields of the Be2 W alloy are generally lower than those of pure beryllium. We found a strong dependence of the sputtering yield on the incident angle with an increase by about a factor of 3 for larger incident angles at 100 eV impact energy. In the pristine surface, a large majority of the impacting hydrogen projectiles at perpendicular impact remains in the surface.