Regensburg 2025 – wissenschaftliches Programm
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HL: Fachverband Halbleiterphysik
HL 51: Transport Properties (joint session HL/TT)
HL 51.3: Vortrag
Donnerstag, 20. März 2025, 15:30–15:45, H13
Ab-initio heat transport in defect-laden quasi-1D systems from a symmetry-adapted perspective — •Yujie Cen1, Sandro Wieser1, Georg Kent Hellerup Madsen1, and Jesús Carrete Montaña2 — 1Institute of Materials Chemistry, TU Wien, A-1060 Wien, Austria — 2Instituto de Nanociencia y Materiales de Aragón (INMA), CSIC-Universidad de Zaragoza, Zaragoza, Spain
Due to their aspect ratio and wide range of thermal conductivities, nanotubes hold significant promise as heat-management nanocomponents. However, one major limitation preventing their widespread use is the typically high thermal resistance that arises from defects or contact with other materials. An intriguing question is the role that structural symmetry plays in thermal transport through those defect-laden sections. However, the ab-initio study of lattice thermal transport is hindered by factors such as the large number of atoms involved and the artifacts introduced by formalism designed for 3D systems.
We employ an Allegro-based machine learning potential to calculate the force constants and phonons of single and multi-layer MoS2-WS2 nanotube with near-DFT accuracy and efficient scaling. Subsequently, we combine representation theory with the mode-resolved Green’s function method to calculate detailed phonon transmission profiles across defects, and connect the transmission probability of each mode to structural symmetry. while more drastic symmetry breakdowns might be expected to increase scattering and thermal resistance, our results show they actually reduce it by the suppression of selection rules and opening more phonon transmission channels.
Keywords: Machine learning potential; Nanotube; Interfacial thermal transport; Green's function; Representation theory