Yang, Sojeong Kim, Seungyun Oda, Takuji 2025-08-20T05:30:48Z 2025-08-20T05:30:48Z 2025-08-20 2025-09 1359-6454 https://pubs.kist.re.kr/handle/201004/152974 Understanding the behavior of hydrogen isotopes is important for predicting mechanical property degradation and tritium retention in plasma-facing materials of fusion reactors. In this study, to assess the fundamental behavior of hydrogen incident on plasma-facing tungsten, we first constructed a machine learning moment tensor potential (MTP) for tungsten-hydrogen systems using density functional theory (DFT) calculation data as a training dataset. Validation tests confirmed that the MTP can reproduce relevant material properties in good agreement with experiments and DFT calculations. We then performed molecular dynamics (MD) calculations using the constructed MTP to simulate the behavior of hydrogen irradiated on the (110) surface of tungsten at 0.1-100 eV. It is confirmed that electronic stopping significantly affects the behavior of incident hydrogen and should therefore be considered in simulations. For hydrogen isotope effects on the sticking coefficient, since electronic stopping has the opposite effect to nuclear stopping, the isotope effects become less significant. With increasing incident energy in the range up to approximately 5 eV, the sticking coefficient decreases due to the increasing probability of reflection at the surface. In contrast, at higher incident energies, the absorption fraction increases because more hydrogen can reach depths below the escape depth, increasing the sticking coefficient. Pre-existing hydrogen atoms on the surface, namely surface coverage, affect not only the sticking probability but also the energy loss behavior and depth profile of the irradiated hydrogen. These results provide a comprehensive picture of the behavior and fate of incident hydrogen in plasma-facing tungsten. English Elsevier BV Sticking, reflection, and abstraction behavior of hydrogen irradiated on (110) tungsten surfaces at 0.1-100 eV by molecular dynamics simulations using a machine learning potential Article 10.1016/j.actamat.2025.121306 1 Acta Materialia, v.297 Acta Materialia 297 N scie scopus 001538686600001 2-s2.0-105011088563 Materials Science, Multidisciplinary Metallurgy & Metallurgical Engineering Materials Science Metallurgy & Metallurgical Engineering Article TOTAL-ENERGY CALCULATIONS ELEY-RIDEAL HOT-ATOM APPROXIMATION RECOMBINATION DISSIPATION DIFFUSIVITY ADSORPTION SCATTERING METALS Machine learning interatomic potential Hydrogen isotopes Tungsten surface Sticking coefficient Abstraction