Understanding the intrinsic thermal transport properties of ultrathin semiconductor nanowires with varying diameters is crucial for the efficient thermal management of next-generation nanoelectronic devices. Here, we developed high-fidelity machine-learning potential (MLP) within the fourth-generation neuroevolution potential framework to elucidate the interplay between structural evolution, amorphous transition behavior, and thermal transport in silicon nanowires (SiNWs), resolving long-standing discrepancies between simulations and experiments. The structure of SiNWs below 1.1 nm in diameter undergoes a complete amorphous transformation, which originates from an amorphous surface structure of 5-6 atomic layers. We identify a nonmonotonic dependence of thermal conductivity on nanowire diameter due to competition between N (Normal) and U (Umklapp) phonon scattering processes. At frequencies <1 THz, N-process scattering rates exceed U-process rates by 3 orders of magnitude in ultrafine SiNWs, enabling fluid-like phonon transport. This study underscores the transformative potential of high-fidelity MLP in unraveling complex nanoscale material behaviors.
Xu et al. (2025) studied this question.