Tailoring mechanical performance in bulk nanoparticle-structured ZnO and Al2O3: Insights from deep learning potential molecular dynamics simulations

The rapid advancements in nanotechnology have created unique opportunities to manipulate material properties at the nanoscale, particularly through the design of nanoparticle-structured (NP-structured) materials. Due to their distinctive mechanical and chemical properties, materials composed of nanoparticles, such as ZnO and Al2O3, are of high interest for applications in structural reinforcements, electronics, and catalysis. However, accurately predicting the mechanical behavior of NP-structured materials remains a challenge. This study investigates the mechanical properties of bulk nanoparticle-structured (NP-structured) materials composed of ZnO and Al2O3 nanoparticles in FCC and BCC configurations. We used deep learning potentials (DLPs) derived from density functional theory (DFT) calculations to comprehensively analyze stress-strain behavior, local shear strain distributions, and elastic properties across various nanoparticle sizes and compositions. Our findings reveal that nanoparticle size and crystalline arrangement are crucial in determining the mechanical performance of these materials. Smaller ZnO nanoparticles exhibit higher yield stress, ultimate stress, and Young's modulus in both FCC and BCC configurations, attributed to their higher surface-to-volume (S/V) ratio. In contrast, larger Al2O3 nanoparticles demonstrated superior mechanical properties, especially in FCC configurations, due to the strong ionic bonding and effective stress distribution inherent in Al2O3. In ZnO/Al2O3composite systems, we found that the mechanical properties could be precisely tuned by adjusting the nanoparticle size and ratio. Larger nano- particles in FCC arrangements contributed to higher yield stress and stiffness, while smaller nanoparticles in BCC arrangements provided better overall mechanical performance. This work highlights the potential of DLPs in accurately predicting and optimizing the mechanical properties of NP-structured materials, offering valuable insights for the design of advanced materials with tailored mechanical characteristics.