Quantitative analysis of aluminum-silicon (Al-Si) alloy microstructure is crucial for evaluating and controlling alloy performance. Conventional analysis methods rely on manual segmentation, which is inefficient and subjective, while fully supervised deep learning approaches require extensive and expensive pixel-level annotated data. Furthermore, existing semi-supervised methods still face challenges in handling the adhesion of adjacent primary silicon particles and effectively utilizing consistency in unlabeled data. To address these issues, this paper proposes a novel semi-supervised framework for Al-Si alloy microstructure image segmentation. First, we introduce a Rotational Uncertainty Correction Strategy (RUCS). This strategy employs multi-angle rotational perturbations and Monte Carlo sampling to assess prediction consistency, generating a pixel-wise confidence weight map. By integrating this map into the loss function, the model dynamically focuses on high-confidence regions, thereby improving generalization ability while reducing manual annotation pressure. Second, we design a Boundary Enhancement Module (BEM) to strengthen boundary feature extraction through erosion difference and multi-scale dilated convolutions. This module guides the model to focus on the boundary regions of adjacent particles, effectively resolving particle adhesion and improving segmentation accuracy. Systematic experiments were conducted on the Aluminum-Silicon Alloy Microstructure Dataset (ASAD). Results indicate that the proposed method performs exceptionally well with scarce labeled data. Specifically, using only 5% labeled data, our method improves the Jaccard index and Adjusted Rand Index (ARI) by 2.84 and 1.57 percentage points, respectively, and reduces the Variation of Information (VI) by 8.65 compared to state-of-the-art semi-supervised models, approaching the performance levels of 10% labeled data. These results demonstrate that the proposed method significantly enhances the accuracy and robustness of quantitative microstructure analysis while reducing annotation costs.

Finding materials with specific properties is a hot topic in materials science. Traditional materials design relies on empirical and trial-and-error methods, requiring extensive experiments and time, resulting in high costs. With the development of physics, statistics, computer science, and other fields, machine learning offers opportunities for systematically discovering new materials. Especially through machine learning-based inverse design, machine learning algorithms analyze the mapping relationships between materials and their properties to find materials with desired properties. This paper first outlines the basic concepts of materials inverse design and the challenges faced by machine learning-based approaches to materials inverse design. Then, three main inverse design methods—exploration-based, model-based, and optimization-based—are analyzed in the context of different application scenarios. Finally, the applications of inverse design methods in alloys, optical materials, and acoustic materials are elaborated on, and the prospects for materials inverse design are discussed. The authors hope to accelerate the discovery of new materials and provide new possibilities for advancing materials science and innovative design methods.