Gradient nano-grained graphene as 2D thermal rectifier: A molecular dynamics based machine learning study

Abstract

Machine learning has become an excellent tool for scientists and engineers to predict, design, and fabricate next-generation material. Here, we report the thermal conductivity and thermal rectification of gradient-nano-grained graphene (GNGG) by molecular dynamic simulation with machine learning. It is revealed that the thermal conductivity of GNGG is mainly determined by the average grain size, while its thermal rectification factor varies linearly with the gradient of nanograins. Deep neural network-based machine learning models are developed to estimate the thermal transport properties of GNGG using microstructural signatures, such as the location, number, and orientation of 5|7 pairs. The results stress the pivotal roles of 5|7 defects in the planar thermal transports of graphene and indicate that high-performance 2D thermal rectifiers for heat flow control and energy harvesting can be achieved by bio-inspired gradient structure engineering. The findings are expected to supply a theoretical strategy for the design of bio-inspired materials and create a method to predict the potential properties of the material candidates by using machine learning, which can save the abundant expense of developing the material by using the classical method.