Metals

The rapid advancement of machine learning (ML) has ushered in a new era for materials science, particularly in the design and understanding of high-entropy alloys (HEAs). As a class of compositionally complex materials, HEAs have greatly benefited from the predictive power and computational efficiency of ML techniques. Recent years have witnessed remarkable expansion in the scope and sophistication of ML applications to HEAs, spanning from phase formation prediction to property and microstructure modeling. These developments have significantly accelerated the discovery and optimization of novel HEA systems. This review provides a comprehensive overview of the current progress and emerging trends in applying ML to HEA research. We first discuss phase prediction methodologies, encompassing both pure ML frameworks and hybrid physics-informed models. Subsequently, we summarize advances in ML-driven prediction of HEA properties and microstructural features. Further sections highlight the role of ML in exploring vast compositional spaces, guiding the design of high-performance HEAs, and optimizing existing alloys through data-driven algorithms. Finally, the challenges and limitations of current approaches are critically examined, and future directions are proposed toward interpretable models, mechanistic understanding, and efficient exploration of the HEA design space.

To address the technical challenge of balancing formability and strength in automotive aluminum alloys, this study examined an Al-4.35Mg-3.6Zn-0.2Cu alloy subjected to a combined heat-treatment schedule consisting of a two-step solution treatment (470 °C for 24 h followed by 460 °C for 30 min) and a subsequent two-step aging process (T4P: 80 °C for 12 h, followed by BH: 180 °C for 30 min). Microstructural evolution was characterized using transmission electron microscopy, and uniaxial tensile tests were performed in accordance with the GB/T 228.1-2021 standard at a strain rate of 0.2 mm/min. In the T4P condition, the matrix contained both GPI zones (~0.9 nm) and GPII zones (~1.2 nm), with no detectable T-phase precipitation. The presence of GPII zones enhanced ductility by promoting dynamic recovery after dislocation shearing, resulting in a yield strength (YS) of 178 MPa, an ultimate tensile strength (UTS) of 310 MPa, and an elongation (El) of 9%. After BH treatment, the GPII zones transformed into semi-coherent T′-Mg₃₂(AlZnCu)₄₉ precipitates (~2.4 nm), which strengthened the alloy through their semi-coherent interfaces. The retained GPII zones mitigated the loss of ductility, and the final mechanical properties reached a YS of 275 MPa, a UTS of 340 MPa, and an El of 8.5%, corresponding to a BH response of 97 MPa. Strengthening-mechanism calculations indicated that GP zones contributed approximately 120 MPa to the yield strength in the T4P state, whereas T′ precipitates contributed about 169.64 MPa after BH treatment. The calculated values agreed well with the experimental results, with a deviation of less than 3%. This study clarifies the precipitation sequence in the alloy—supersaturated solid solution → GPI zones → GPII zones → T′ phase—and establishes the relationship between microstructure and strength–ductility behavior. The findings provide theoretical guidance for the design and optimization of high-strength, high-formability aluminum alloys for automotive outer-panel applications.

In the context of growing demands for durable, high-performance materials capable of operating in increasingly harsh environments, metallic glasses and their composites have attracted extensive research interest. Metallic glasses and their composites exhibit remarkable advantages for structural applications in a wide range of environments due to their unique disordered atomic structure, high strength, and excellent physicochemical properties. In recent years, their corrosion behavior has garnered broad attention, with particular emphasis on corrosion resistance under complex service conditions becoming a central research focus. This review provides a comprehensive examination of the corrosion behavior of metallic glass and their composites. It surveys the reaction mechanisms and characteristic features of these materials in diverse corrosive media, analyzes the factors that govern their corrosion resistance, and summarizes strategies for optimizing their corrosion performance, with the aim of promoting their application in real-world service environments.