Machine learning-based design of porous graphene with low thermal conductivity

The thermal conductivity of two-dimensional materials like graphene can efficiently be tuned by introducing holes, in which the density and distribution of the holes are the key parameters. Furthermore, the distribution of holes can induce a variation as high as 74% in the thermal conductivity for porous graphene with a given density of holes. Therefore, an existing challenge is to find the optimal distribution of holes that can minimize or maximize the thermal conductivity of porous graphene as the design space expands dramatically with increasing hole density. We therefore apply an inverse design methodology based on machine learning to reveal the relationship between hole distribution and thermal conductivity reduction in monolayer graphene. The methodology reveals that holes that are randomly distributed transverse to the direction of heat flow, but that exhibit some periodicity along the direction of heat flow, represent the optimal distribution to minimizing the thermal conductivity for porous graphene. Lattice dynamics calculations and wave packet simulations reveal that this spatial distribution effectively causes localization of the phonon modes in porous graphene, which reduces the thermal conductivity. Overall, this work demonstrates the power of machine learning-based design approaches to efficiently obtain new physical insights for scientific problems of interest.