Regensburg 2025 – wissenschaftliches Programm
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MM: Fachverband Metall- und Materialphysik
MM 27: Transport in Materials: Diffusion, Charge or Heat Conduction
MM 27.1: Vortrag
Donnerstag, 20. März 2025, 10:15–10:30, H22
Modelling Heat Transport in Metal-Organic Frameworks with Machine Learned Potentials — •Martin Klotz1, Florian Lindner1, Sandro Wieser2, and Egbert Zojer1 — 1Institute of Solid State Physics, Graz University of Technology, Austria — 2Institute of Materials Chemistry, TU Wien, Austria
Many of the envisioned applications of porous metal-organic frameworks (MOFs), like gas storage or catalysis, involve exothermal processes. This requires the materials to efficiently dissipate heat. It is, thus, vital to gain a fundamental understanding of how structural and chemical modifications impact heat-transport in such systems. Here, we chose MOF-74/Zn as parent system for which we calculated the anisotropic thermal conductivity using approach to equilibrium molecular dynamics simulations. For these, we applied machine-learned force field potentials, as they surpass DFT simulations by orders of magnitudes in terms of speed and classically parametrized force field potentials in terms of accuracy. Subsequent to thoroughly benchmarking and testing the employed methodology, we studied structure-to-property relationships for MOF-74 derivatives, systematically varying the node metal and the linker structure.
Keywords: Heat-transport simulation; Machine learning; Moment tensor potential; Metal-organic framework