Phonon Bottleneck Identification in Disordered Nanoporous Materials

Nanostructured materials are promising as thermoelectrics since thermal transport can be suppressed with little degradation in electrical properties, a crucial requirement for high-efficiency thermal energy conversion. Porous materials offer a flexible platform thanks to their extensive range of possible configurations and robust nature compared to other nanostructures. Although there has been great progress in modeling the thermal properties of nanoporous materials, it has remained a challenge to screen such materials over a large phase space due to the slow simulation time required for accurate results. This has left a gap in our ability to optimize nanoporous thermoelectrics both in designing ordered, ideal porous structures as well as in understanding the role of pore disorder. In this work, we use density functional theory in connection with the Boltzmann transport equation, to perform high-throughput calculations of heat transport in disordered porous materials. By leveraging graph theory and regressive analysis, we identify the set of pores representing the most effective phonon bottleneck. The strength of such bottlenecks is assessed by the “repeated bottleneck” algorithm, which ultimately provides a measure of the effect of disorder on thermal transport. These results shed light on thermal conductivity reduction in disordered porous materials and provide a simple tool to estimate phonon suppression in realistic porous materials.