Thermal conductivities of monolayer graphene oxide from machine learning molecular dynamics simulations

TL;DR

This study uses machine learning-enhanced molecular dynamics to predict how chemical reduction affects the thermal conductivity of graphene oxide, revealing significant suppression and complex dependencies on oxidation levels.

Contribution

Develops a machine-learned neuroevolution potential for large-scale MD simulations to quantitatively link reduction chemistry with heat transport in graphene oxide.

Findings

Reduced GO shows thermal conductivities from a few to tens of W/mK, much lower than pristine GO.

Thermal conductivity increases with OH/O ratio but decreases with O/C ratio, especially at high oxidation levels.

The framework enables predictive modeling of heat transport based on chemical structure in heterogeneous carbon materials.

Abstract

Graphene oxide (GO) exhibits rich chemical heterogeneity that strongly influences its structural, thermal, and mechanical properties, yet quantitatively linking reduction chemistry to heat transport remains challenging. In this work, we develop a machine-learned neuroevolution potential (NEP) trained on an existing density functional theory dataset (\textit{Angew.\ Chem.\ Int.\ Ed.}, \textbf{63} , e202410088 (2024)), achieving reasonable accuracy at a computational cost much lower than the existing machine-learned and empirical potentials. Leveraging this potential, we perform large-scale molecular dynamics (MD) simulations to model the thermal reduction of GO across realistic structural domains. Using the homogeneous nonequilibrium MD method with a proper quantum-statistical correction scheme, we find that reduced GO exhibits strongly suppressed thermal conductivities, ranging from a few to tens of Wm$^{-1}$K$^{-1}$, substantially lower than pristine GO without defects and far below graphene. Moreover, the thermal conductivity of reduced GO increases moderately with increasing OH/O ratio, except at the highest oxidation level (O/C=0.5) where this trend inverts, while decreasing significantly with increasing O/C ratio, a trend strongly correlated with the fraction of recovered graphene-like structures. Our work provides a computationally tractable and predictive atomistic machine learning framework for exploring how chemical structure governs heat transport in heterogeneous carbon materials.