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The microstructure and mechanical properties have a significant impact on the service performance of materials. This paper compared the microstructure and mechanical properties of 2024 aluminum alloy subjected to turning and ultrasonic surface rolling process (USRP). It was found that both turning and USRP can cause grain refinement and increased microhardness. The grain refinement induced by USRP was attributed to dislocation proliferation and T-phase fragmentation; At the same time, a quantitative relationship model between microhardness and microstructure during turning and USRP was constructed. The results showed that precipitation had the greatest impact on the microhardness induced by turning, while dislocations and grain refinement had the greatest impact on the microhardness induced by USRP; In addition, the mechanical performance response mechanism of the materials after turning and USRP was elucidated, and it was found that USRP can significantly enhance the yield strength of the materials.

The fatigue load that a material experiences and its microstructure are important factors influencing fatigue crack propagation behavior. This study employed laser scanning microscopy and electron backscatter diffraction (EBSD) technology, along with fatigue crack propagation experiments, to investigate the fatigue crack propagation behavior of 2024 aluminum alloy under varying stress ratios (R). The results showed that the stress amplitude (σp) was the main factor controlling the fatigue crack propagation life (N). Additionally, detailed characterization of the fatigue crack propagation path was conducted using crystal models and EBSD. A fatigue crack propagation model for 2024 aluminum alloy under different R-values was established based on the grain twist angle and Schmid factor. Finally, the impact of R on the crack tip shielding (Ks) was systematically analyzed, elucidating the intrinsic mapping relationship between R, microstructure, and crack propagation characteristics.

As the core material that affects the performance of OLED devices, the demand for performance improvement of OLED materials is urgent. However, due to the molecular diversity of organic compounds, traditional methods of development are inefficient and expensive. Recently, the machine learning (ML) approach has attracted increasing attention in the field of organic luminescent materials, which can learn from the existing results and provide the relation between the input features to the output performance. By constructing the relationship between molecular structure and device performance of OLED, the efficiency of material development is higher and guidance for material design is better provided through the identification of molecular key factors. In this work, we attempt to use the ML approach to explore the quantificational relation between the external quantum efficiency (EQE) of red phosphorescent organic light‐emitting devices (OLEDs) and their material and device structural factors, aiming to screen out the key factors governing the EQE and predict EQE values directly by molecular structure. We established the dataset based on over 1000 device data from experiment, and reduced the number of molecular descriptors to below 35. Currently, the root mean squared error (RMSE) of test set has been lowered to 3.21%. These results provide essential guidance for material screening and experimental device optimization. On this basis, we further designed new red host materials, which have higher EQE, up to 27.56%. It has reached the first‐class level of commercial materials. [ABSTRACT FROM AUTHOR]