Integrated computational materials engineering (ICME) has significantly enhanced the systemic analysis of the relationship between microstructure and material properties, paving the way for the development of high-performance materials. However, analyzing microstructure-sensitive material behavior remains challenging due to the scarcity of three-dimensional (3D) microstructure datasets. Moreover, this challenge is amplified if the microstructure is anisotropic, as this results in anisotropic material properties as well. In this paper, we present a framework for reconstruction of anisotropic microstructures solely based on two-dimensional (2D) micrographs using conditional diffusion-based generative models (DGMs). The proposed framework involves spatial connection of multiple 2D conditional DGMs, each trained to generate 2D microstructure samples for three different orthogonal planes. The connected multiple reverse diffusion processes then enable effective modeling of a Markov chain for transforming noise into a 3D microstructure sample. Furthermore, a modified harmonized sampling is employed to enhance the sample quality while preserving the spatial connection between the slices of anisotropic microstructure samples in 3D space. To validate the proposed framework, the 2D-to-3D reconstructed anisotropic microstructure samples are evaluated in terms of both the spatial correlation function and the physical material behavior. The results demonstrate that the framework is capable of reproducing not only the statistical distribution of material phases but also the material properties in 3D space. This highlights the potential application of the proposed 2D-to-3D reconstruction framework in establishing microstructure-property linkages, which could aid high-throughput material design for future studies

翻译：集成计算材料工程显著增强了对微观结构与材料性能之间关系的系统性分析，为高性能材料的开发奠定了基础。然而，由于三维微观结构数据集的稀缺性，分析微观结构敏感的材料行为仍面临挑战。当微观结构呈现各向异性时，这一挑战更为突出，因为各向异性微观结构同样会导致材料性能的各向异性。本文提出了一种基于条件扩散生成模型的框架，仅利用二维显微图像即可实现各向异性微观结构的重建。该框架涉及多个二维条件扩散生成模型的空间连接，每个模型分别针对三个不同正交平面的二维微观结构样本生成进行训练。通过连接多个反向扩散过程，有效构建了将噪声转化为三维微观结构样本的马尔可夫链。此外，采用改进的谐调采样方法，在保持各向异性微观结构切片三维空间连接性的同时提升样本质量。为验证所提框架，从空间相关函数和物理材料行为两个维度对二维到三维重建的各向异性微观结构样本进行了评估。结果表明，该框架不仅能够复现材料相的统计分布，还能在三维空间中复现材料性能。这凸显了所提出的二维到三维重建框架在建立微观结构-性能关联方面的潜在应用价值，可为未来高通量材料设计研究提供支撑。