To determine the effects of Ti and mixing entropy (ΔS_mix) on the structure and mechanical proper-ties of Zr-Ta alloys and then find a new potential energetic structural material with good me-chanical properties and more reactive elements, Ti_xZr_2.5−xTa (x = 0, 0.5, 1.0, 1.5, 2.0) alloys were investigated. The XRD experimental results showed that the phase transformation of Ti_xZr_2.5−xTa nonequal-ratio ternary alloys depended not on the value of ΔS_mix, but on the amount of Ti atoms. With the addition of Ti, the content of the HCP phase decreased gradually. SEM analyses revealed that dendrite morphology and component segregation increasingly developed and then weakened gradually. When x increases to 2.0, Ti_xZr_2.5−xTa with the best mechanical properties can be ob-tained. The yield strength, compressive strength and fracture strain of Ti_2.0Zr_0.5Ta reached 883 MPa, 1568 MPa and 34.58%, respectively. The dependence of the phase transformation and me-chanical properties confirms that improving the properties of Zr-Ta alloys by doping Ti is feasible.

This study developed a twinning-induced plasticity (TWIP) steel characterized by lightweight, high strength, and high toughness. Tensile tests were conducted at strain rates ranging from 10⁻⁴ to 6500 s⁻¹ using a universal testing machine and a Hopkinson bar to evaluate the material’s mechanical properties. A Johnson–Cook (J-C) constitutive model was developed based on the mechanical performance data for high-strain behavior. X-ray diffraction (XRD), scanning electron microscopy (SEM), and electron backscatter diffraction (EBSD) were employed to analyze the microstructural evolution and fracture mechanisms of tensile specimens. The results show that the TWIP steel exhibits positive strain rate sensitivity (PSRS) under both quasi-static and dynamic strain rates. At high strain rates, the yield strength increased from 1133.0 MPa to 1430.6 MPa, and the tensile strength rose from 1494.3 MPa to 1640.34 MPa. The J-C model fits well at strain rates of 1000 s⁻¹ and 3000 s⁻¹, but fitting errors increase at higher strain rates due to the competition between thermal softening and strain hardening. XRD results reveal no significant phase transformation occurred during deformation, with twinning being the dominant mechanism. As the strain rate increased, deformation twins appeared in the material’s microstructure, inducing plastic deformation during tensile testing. The twin volume fraction increases progressively with the strain rate. At high strain rates, secondary twins emerge and intersect with primary twins, refining the grains through mutual interaction. The TWIP effect enhances the material’s mechanical performance by improving its strength and ductility while maintaining its lightweight nature.

The rapid evolution of artificial intelligence, particularly large language models, presents unprecedented opportunities for materials science research. We proposed and developed an AI materials scientist named MatPilot, which has shown encouraging abilities in the discovery of new materials. The core strength of MatPilot is its natural language interactive human-machine collaboration, which augments the research capabilities of human scientist teams through a multi-agent system. MatPilot integrates unique cognitive abilities, extensive accumulated experience, and ongoing curiosity of human-beings with the AI agents' capabilities of advanced abstraction, complex knowledge storage and high-dimensional information processing. It could generate scientific hypotheses and experimental schemes, and employ predictive models and optimization algorithms to drive an automated experimental platform for experiments. It turns out that our system demonstrates capabilities for efficient validation, continuous learning, and iterative optimization.