Microstructure reconstruction serves as a crucial foundation for establishing Process-Structure-Property (PSP) relationship in material design. Confronting the limitations of variational autoencoder and generative adversarial network within generative modeling, this study adopted the denoising diffusion probability model (DDPM) to learn the probability distribution of high-dimensional raw data and successfully reconstructed the microstructures of various composite materials, such as inclusion materials, spinodal decomposition materials, chessboard materials, fractal noise materials, and so on. The quality of generated microstructure was evaluated using quantitative measures like spatial correlation functions and Fourier descriptor. On this basis, this study also successfully achieved the regulation of microstructure randomness and the generation of gradient materials through continuous interpolation in latent space using denoising diffusion implicit model (DDIM). Furthermore, the two-dimensional microstructure reconstruction is extended to three-dimensional framework and integrates permeability as a feature encoding embedding. This enables the conditional generation of three-dimensional microstructures for random porous materials within a defined permeability range. The permeabilities of these generated microstructures were further validated through the application of the Boltzmann method.

翻译：微结构重建是建立材料设计中工艺-结构-性能关系的关键基础。针对变分自编码器和生成对抗网络在生成建模中的局限性，本研究采用去噪扩散概率模型学习高维原始数据的概率分布，成功重建了多种复合材料的微结构，如夹杂材料、旋节线分解材料、棋盘材料、分形噪声材料等。通过空间相关函数和傅里叶描述子等量化指标评估生成微结构的质量。在此基础上，本研究还利用去噪扩散隐式模型在隐空间中进行连续插值，成功实现了微结构随机性的调控以及梯度材料的生成。此外，将二维微结构重建拓展至三维框架，并融入渗透率作为特征编码嵌入，从而能够在指定渗透率范围内对随机多孔材料的三维微结构进行条件生成。通过应用玻尔兹曼方法进一步验证了生成微结构的渗透率。