Al-Li alloys are widely used in aerospace applications due to their high strength, high fracture toughness, and strong resistance to stress corrosion. However, the lack of interatomic potentials has hindered systematic investigations of the relationship between structures and properties. To address this issue, we apply a neural network-based neuroevolutionary machine learning potential (NEP) and use evolutionary strategies to train it for large-scale molecular dynamics (MD) simulations. The results obtained from this potential function are compared with those from Density Functional Theory (DFT) calculations, with training errors of 2.1 meV/atom for energy, 47.4 meV/Å for force, and 14.8 meV/atom for virial, demonstrating high training accuracy. Using this potential, we simulate cluster formation and the high-temperature stability of the T1 phase, with results consistent with previous experimental findings, confirming the accurate predictive capability of this potential. This approach provides a simple and efficient method for predicting atomic motion, offering a promising tool for the thermal treatment of Al-Li alloys.

Al-Zn-Mg-Cu alloys are widely used as heat-treatable ultra-high-strength materials in aerospace structural applications. While conventional single-stage aging enables high strength, advanced performance demands call for precise microstructural control via multi-stage aging. In this study, we employ a combination of scanning transmission electron microscopy (STEM), energy-dispersive X-ray spectroscopy (EDS), and X-ray diffraction (XRD) to investigate the microstructural evolution and its correlation with mechanical properties of AA7150 aluminum alloy subjected to two-step aging treatments, following a 6 h pre-aging at 120 °C. Through atomic-scale STEM imaging along the [110]_Al zone axis, we systematically characterize the precipitation behavior of GPII zones, η′ phases, and equilibrium η phases both within the grains and at grain boundaries under varying secondary aging (SA) conditions. Our results reveal that increasing the SA temperature from 140 °C to 180 °C leads to coarsening and reduced number density of intragranular precipitates, while promoting the continuous and coarse precipitation of η phases along grain boundaries, accompanied by a widening of the precipitation-free zone (PFZ). Notably, SA at 160 °C induces the formation of fine, uniformly dispersed nanoscale η′ precipitates in the alloy, as confirmed by XRD phase analysis. Aging at this temperature markedly enhances the mechanical properties, achieving an ultimate tensile strength (UTS) of 613 MPa and a yield strength (YS) of 598 MPa, while presenting an exceptionally broad peak-aging plateau. Owing to this feature, a moderate extension of the SA duration does not reduce strength and can further improve ductility, increasing the elongation (EL) to 14.26%. These results demonstrate a novel two-step heat-treatment strategy that simultaneously achieves ultra-high strength and excellent ductility, highlighting the critical role of advanced electron microscopy in elucidating phase-transformation pathways that inform microstructure-guided alloy design and processing.

The primary objective of modal identification for variable thickness quartz plates is to ascertain their dominant operating mode, which is essential for examining the vibration of beveled quartz resonators. These beveled resonators are plate structures with varying thicknesses. While the beveling process mitigates some spurious modes, it still presents challenges for modal identification. In this work, we introduce a modal identification technique based on the energy method. When a plate with variable thickness is in a resonant state of thickness–shear vibration, the proportions of strain energy and kinetic energy associated with the thickness–shear mode in the total energy reach their peak values. Near this frequency, their proportions are the highest, aiding in identifying the dominant mode. Our research was based on the Mindlin plate theory, and appropriate modal truncation were conducted by retaining three modes for the coupled vibration analysis. The governing equation of the coupled vibration was solved for eigenvalue problem, and the modal energy proportions were calculated based on the determined modal displacement and frequency. Finally, we computed the eigenvalue problems at different beveling time, as well as the modal energies associated with each mode. By calculating the energy proportions, we could clearly identify the dominant mode at each frequency. Our proposed method can effectively assist engineers in identifying vibration modes, facilitating the design and optimization of variable thickness quartz resonators for sensing applications.