The application of tungsten (W) as plasma-facing materials (PFMs) in nuclear fusion reactors is limited by its low recrystallization temperature (RCT) and significant grain growth at elevated temperatures. Potassium (K)-doped W exhibits remarkably enhanced thermal stability due to nanoscale K bubbles, yet the atomic-scale pinning mechanism remains unclear. In this study, a high-precision neuroevolution potential (NEP) for the W-K system was developed using machine learning, and molecular dynamics (MD) simulations were performed to elucidate the unique pinning effect of the K bubbles on grain boundary (GB) migration. Our findings reveal that K bubbles exhibit distinct pinning characteristics compared to solid second-phase particles. Through quantification of GB migration velocity changes before and after the K bubble incorporation, the average pinning forces exerted by bubbles of different sizes were calculated, all of which were found to significantly exceed the maximum pinning forces predicted by classical Zener theory for solid particles of equivalent dimensions. The exceptional pinning capability is attributed to the high deformability of K bubbles, which exhibit substantial elongation and surface wrinkling during GB interactions, consequently expanding the contact area and amplifying pinning effectiveness. The findings lead to the proposition of a "deformation-enhanced pinning" mechanism that fundamentally accounts for the remarkable grain growth suppression achieved by trace K doping (<100 ppm). This work provides crucial theoretical guidance for designing advanced PFMs with superior thermal stability, paving the way for developing next-generation fusion materials.
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