Machine learning-assisted study on the thermal transport properties of two-dimensional M3(C6O6)2 (M=Fe, Co, Ni) materials.

Abstract

Two-dimensional metal-organic frameworks (MOF) are widely used in electronic devices and energy storage due to their large surface area, abundant active sites, and tunable sizes. A deeper understanding of the thermal transport properties of two-dimensional MOF materials is essential for these applications. In this work, we systematically studied the thermal transport properties of M3(C6O6)2 (M = Fe, Co, Ni) by using a machine learning interatomic potential (MLIP) method combined with the phonon Boltzmann transport equation (PBTE). The results show that the lattice thermal conductivities of Fe3(C6O6)2, Co3(C6O6)2, and Ni3(C6O6)2 at room temperature are 4.0 W/mK, 5.5 W/mK, and 5.8 W/mK, respectively. The differences in thermal conductivity primarily arise from variations in phonon relaxation times, which can be elucidated by examining the three-phonon scattering phase space. Further analysis of bond strengths reveals that the strong bonding between Fe and O impedes phonon propagation through the oxygen atoms, resulting in lower lattice thermal conductivity. Our work provides a fundamental reference for understanding thermal transport in two-dimensional MOF. .