Deep-learning-driven modeling of oxygen influence on graphene oxide elastic modulus

Graphene oxide (GO) and its reduced forms exhibit versatile structures and exceptional mechanical strength, making their nanocomposites attractive for applications requiring high elastic modulus. Oxygen functional groups critically modulate the elastic modulus of GO, yet molecular-level understanding remains limited, as existing simulation results have struggled to reproduce experimentally observed trends due to challenges in accurate modeling and calculations. In this study, a deep learning potential field with near-density functional theory accuracy is developed for molecular dynamics (MD) simulations of GO. X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR) patterns serve as unique fingerprints to identify specific GO structures. GO models fitted to experimental XRD/FTIR data yield elastic modulus predictions with less than 1% deviation. Systematic exploration of C/O ratios reveals that the elastic modulus first increases and then decreases with increasing carbon content. Epoxy-rich GO demonstrates a larger modulus reduction than hydroxyl- or carboxyl-rich counterparts. Bond-length distribution analysis shows enhanced sp² bond energy in regions of mixed sp²/sp³ hybridization, correlating with modulus trends. Unlike previous MD studies, which reported that the elastic modulus remains below that of pristine graphene with the addition of oxygen content, our simulation, by tuning the sp²/sp³ hybrid domain, reproduces the experimentally observed phenomenon with an elastic modulus 19% higher than that of pristine graphene. This study reveals the role of oxygen functional groups proportion and types in modulating sp²/sp³ hybridization and mechanical strength, providing a foundation for the design of high-modulus graphene structures with preserved oxygen content, suited for applications such as tissue engineering, aerospace, and structural engineering.