Mechanical properties of graphene oxide from machine-learning-driven simulations.

Journal: Chemical communications (Cambridge, England)
Published Date: Jul 24, 2025

Abstract

Graphene oxide (GO) materials have complex chemical structures that are linked to their macroscopic properties. Here we show that first-principles simulations with a machine-learned interatomic potential can predict the mechanical properties of GO sheets in agreement with experiment and provide atomistic insights into the mechanisms of strain and fracture. Our work marks a step towards understanding and controlling mechanical properties of carbon-based materials with the help of atomistic machine learning.

Authors

  • Zakariya El-Machachi
    Inorganic Chemistry Laboratory, Department of Chemistry, University of Oxford, Oxford OX1 3QR, UK. volker.deringer@chem.ox.ac.uk.
  • Bowen Cheng
    Inorganic Chemistry Laboratory, Department of Chemistry, University of Oxford, Oxford OX1 3QR, UK. volker.deringer@chem.ox.ac.uk.
  • Volker L Deringer
    Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford Oxford OX1 3QR UK volker.deringer@chem.ox.ac.uk.

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