Density dependence of thermal conductivity in nanoporous and amorphous carbon with machine-learned molecular dynamics

Disordered forms of carbon are an important class of materials for applications such as thermal management. However, a comprehensive theoretical understanding of the structural dependence of thermal transport and the underlying microscopic mechanisms is lacking. Here we study the structure-dependent thermal conductivity of disordered carbon by employing molecular dynamics (MD) simulations driven by a machine-learned interatomic potential based on the efficient neuroevolution potential approach. Using large-scale MD simulations, we generate realistic nanoporous carbon (NP-C) samples with densities varying from 0.3 to 1.5 g cm-3 dominated by sp2 motifs, and amorphous carbon (a-C) samples with densities varying from 1.5 to 3.5 g cm-3 exhibiting mixed sp2 and sp3 motifs. Structural properties including short- and medium-range order are characterized by the atomic coordination, pair correlation function, angular distribution function, and structure factor. Using the homogeneous nonequilibrium MD method and the associated quantum-statistical correction scheme, we predict a linear and a superlinear density dependence of thermal conductivity for NP-C and a-C, respectively, in good agreement with relevant experiments. The distinct density dependences are attributed to the different impacts of the sp2 and sp3 motifs on the spectral heat capacity, vibrational mean free paths, and group velocity. We additionally highlight the significant role of structural order in regulating the thermal conductivity of disordered carbon.