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

通过基于二维显微图的条件扩散生成模型 (DGMs) 的空间连接，我们提出了一个重建各向异性微结构的框架，该框架能够有效地将噪声转化为三维微结构样本，并且通过修改的协调抽样方法提高样本质量，同时保持各向异性微结构样本在三维空间中的空间连接。验证结果表明，该框架能够再现材料相的统计分布和三维空间中的材料性质，凸显了该二维至三维重建框架在建立微结构 - 性能关联中的潜在应用，为未来研究中的高通量材料设计提供了帮助。

基于扩散的去噪合成三维各向异性微结构的二维映像转换

Denoising diffusion-based synthetic generation of three-dimensional (3D)  anisotropic microstructures from two-dimensional (2D) micrographs

Acquiring reliable microstructure datasets is a pivotal step toward the
systematic design of materials with the aid of integrated computational
materials engineering (ICME) approaches. However, obtaining three-dimensional
(3D) microstructure datasets is often challenging due to high experimental
costs or technical limitations, while acquiring two-dimensional (2D)
micrographs is comparatively easier. To deal with this issue, this study
proposes a novel framework for 2D-to-3D reconstruction of microstructures
called Micro3Diff using diffusion-based generative models (DGMs). Specifically,
this approach solely requires pre-trained DGMs for the generation of 2D
samples, and dimensionality expansion (2D-to-3D) takes place only during the
generation process (i.e., reverse diffusion process). The proposed framework
incorporates a new concept referred to as multi-plane denoising diffusion,
which transforms noisy samples (i.e., latent variables) from different planes
into the data structure while maintaining spatial connectivity in 3D space.
Furthermore, a harmonized sampling process is developed to address possible
deviations from the reverse Markov chain of DGMs during the dimensionality
expansion. Combined, we demonstrate the feasibility of Micro3Diff in
reconstructing 3D samples with connected slices that maintain morphologically
equivalence to the original 2D images. To validate the performance of
Micro3Diff, various types of microstructures (synthetic and experimentally
observed) are reconstructed, and the quality of the generated samples is
assessed both qualitatively and quantitatively. The successful reconstruction
outcomes inspire the potential utilization of Micro3Diff in upcoming ICME
applications while achieving a breakthrough in comprehending and manipulating
the latent space of DGMs.

使用扩散型生成模型 (DGMs) 的 2D 到 3D 映射框架 Micro3Diff，使得通过仅需预先训练 DGMs 进行 2D 样本生成，并在生成过程中进行维度扩展的方式实现了对连接切片，保持与原始 2D 图像形态等价的 3D 样本的重建，具备在集成计算材料工程应用中潜在利用价值，并突破对 DGMs 潜空间的理解和操作。

基于多平面去噪扩展的微结构二维到三维重建与采样协调

Multi-plane denoising diffusion-based dimensionality expansion for  2D-to-3D reconstruction of microstructures with harmonized sampling

The European Materials and Modelling Ontology (EMMO) is a top-level ontology
designed by the European Materials Modelling Council to facilitate semantic
interoperability between platforms, models, and tools in computational
molecular engineering, integrated computational materials engineering, and
related applications of materials modelling and characterization. Additionally,
domain ontologies exist based on data technology developments from specific
platforms. The present work discusses the ongoing work on establishing a
European Virtual Marketplace Framework, into which diverse platforms can be
integrated. It addresses common challenges that arise when marketplace-level
domain ontologies are combined with a top-level ontology like the EMMO by
ontology alignment.

该论文讨论了欧洲材料建模理事会设计的欧洲材料与建模本体（EMMO），以促进计算分子工程、集成计算材料工程和相关材料建模和表征应用程序中的平台、模型和工具之间的语义互操作性。此外，本文介绍了关于建立一个欧洲虚拟市场框架的进行中的工作，其中可以集成不同的平台。同时，还探讨了市场级别的领域本体与像 EMMO 这样的顶层本体结合时所面临的共同挑战。