Accelerated First-Principles Exploration of Structure and Reactivity in Graphene Oxide

TL;DR

This paper introduces a machine-learning accelerated approach combining first-principles molecular dynamics and neural-network potentials to efficiently explore the atomic structures and reactivity of graphene oxide, aligning well with experimental data.

Contribution

It presents a novel, rapid method for sampling and predicting the structures and reactions of graphene oxide using advanced machine learning techniques.

Findings

Consistent with experimental observations of GO reduction.

Provides atomistic insights into GO reactivity.

Establishes a platform for predictive simulations of carbon materials.

Abstract

Graphene oxide (GO) materials are widely studied, and yet their atomic-scale structures remain to be fully understood. Here we show that the chemical and configurational space of GO can be rapidly explored by advanced machine-learning methods, combining on-the-fly acceleration for first-principles molecular dynamics with message-passing neural-network potentials. The first step allows for the rapid sampling of chemical structures with very little prior knowledge required; the second step affords state-of-the-art accuracy and predictive power. We apply the method to the thermal reduction of GO, which we describe in a realistic (ten-nanometre scale) structural model. Our simulations are consistent with recent experimental findings and help to rationalise them in atomistic and mechanistic detail. More generally, our work provides a platform for routine, accurate, and predictive simulations of diverse carbonaceous materials.