Sputtering and reflection from a beryllium surface: effects of hydrogen isotope mass, impact position and surface binding energy

NUCLEAR FUSION(2022)

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摘要
Atomistic simulations with machine-learned potential energy functions are employed for understanding the mechanisms driving the sputtering of beryllium by low-energy deuterium and tritium atoms and the details of their retention on pristine beryllium surfaces. The interaction between hydrogen/deuterium/tritium and beryllium surfaces regarding erosion yields is investigated by molecular dynamics simulations. The erosion yields of both hydrogen isotopes are similar for the same kinetic energies. Concentrating on deuterium, its impact on specific surface sites is analyzed. Finally, analytical expressions are used to predict the energy spectra of sputtered atoms.
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关键词
sputtering simulations, molecular dynamics, plasma-surface interaction, machine learned potential energy function, neural networks, computational materials science
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