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Oxidation-dependent mechanical response of graphene oxide: Improving the reliability of atomistic modeling by ab initio machine learning simulations

Sara Shahbazi Fashtali1,*, Pablo M. Piaggi2,3, and Giuseppe Zollo1

  • 1Dipartimento di Scienze di Base e Applicate per l'Ingegneria, Sapienza University of Rome, Via A. Scarpa 14–16, 00161 Rome, Italy
  • 2CIC nanoGUNE, Tolosa Hiribidea 76, 20018 Donostia-San Sebastián, Spain
  • 3Ikerbasque, Basque Foundation for Science, 48013 Bilbao, Spain

  • *Contact author: sara.shahbazifashtali@uniroma1.it
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Phys. Rev. Materials 10, 113601 – Published 2 November, 2026

DOI: https://doi.org/10.1103/vwr6-mt41

Abstract

The mechanical response of graphene oxide (GO) is controlled by oxidation level and functional group composition, yet a clear atomic level picture remains ambiguous due to limitations of classical reactive force fields. Here, we develop a machine learning interatomic potential by fine-tuning a MACE foundation model on density functional theory data. We employ this potential to systematically investigate GO tensile behavior across oxidation levels of 10% to 40% and hydroxyl-to-epoxy (OH/O) ratios of 0 to 3. Our potential accurately reproduces the expected decrease in Young's modulus and ultimate tensile strength with increasing oxidation, correcting the overestimation and dispersion characteristic of ReaxFF predictions. In the elastic and plastic regimes, oxidation level dominates the mechanical response, while the functional group identity remains secondary. In the fracture regime, however, chemical composition becomes significant, controlling ductile/brittle behavior. In highly oxidized, hydroxyl-rich systems, stress relaxation via dehydration, not captured by reactive force fields, critically influences fracture behavior.

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Phys. Rev. Materials 10, 113601– Published 2 November, 2026
  • Received 24 July 2026
  • Accepted 5 October 2026
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