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Metal-organic frameworks (MOFs) are a family of materials that have highporosity and structural tunability and hold great potential in variousapplications, many of which requiring a proper understanding of the thermaltransport properties. Molecular dynamics (MD) simulations play an importantrole in characterizing the thermal transport properties of various materials.However, due to the complexity of the structures, it is difficult to constructaccurate empirical interatomic potentials for reliable MD simulations of MOFs.To this end, we develop a set of accurate yet highly efficient machine-learnedpotentials for three typical MOFs, including MOF-5, HKUST-1, and ZIF-8, usingthe neuroevolution potential approach as implemented in the GPUMD package, andperform extensive MD simulations to study thermal transport in the three MOFs.Although the lattice thermal conductivity (LTC) values of the three MOFs areall predicted to be smaller than 1 $\rm{W/(m\ K)}$ at room temperature, thephonon mean free paths (MFPs) are found to reach the sub-micrometer scale inthe low-frequency region. As a consequence, the apparent LTC only converges tothe diffusive limit for micrometer single crystals, which means that the LTC isheavily reduced in nanocrystalline MOFs. The sub-micrometer phonon MFPs arealso found to be correlated with a moderate temperature dependence of LTCbetween those in typical crystalline and amorphous materials. Both the largephonon MFPs and the moderate temperature dependence of LTC fundamentally changeour understanding of thermal transport in MOFs.

Sub-micrometer phonon mean free paths in metal-organic frameworks revealed by machine-learning molecular dynamics simulations