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.

Due to their excellent specific strength and lightweight characteristics, Al-Cu-Li alloys are widely used in aerospace applications. The newly developed three-stage creep aging (CA) process ensures both the formability and high performance of the Al alloy. However, research at the atomic scale investigating the relationship between the microstructure and performance of ternary alloys under intricate heat treatment conditions remains scarce. This study investigates the microstructural evolution of Al-Cu-Li alloys during multi-stage low-high-low temperature CA experiments, combined with molecular dynamics (MD) simulations based on the neuroevolutionary machine learning potential (NEP) function. The simulation results indicate that the segregation state of lithium atoms at low temperatures is unstable and cannot persist at elevated temperatures. As the aging temperature in the second stage increases, the segregation of lithium atoms gradually diminishes. However, the low-temperature aging in the third stage facilitates continued atomic segregation, although the recovery is somewhat limited. Additionally, it was observed that high-temperature aging in the second stage reduces the material’s performance, while the low-temperature aging in the third stage contributes to the recovery of its properties. The experimental results indicate that the degree of precipitation phase enrichment decreases with the increase in temperature during the second stage but slightly increases with the low-temperature aging in the third stage. The excellent agreement between the experimental and simulation results validates the reliability of the MD simulations, providing a valuable reference for the performance enhancement and microstructural optimization of Al-Cu-Li alloys.