用扩散模型生成可计算的三维多晶结构，精准控制晶粒形态与性能。
PolyCrysDiff: Controllable Generation of Three-Dimensional Computable Polycrystalline Material Structures
- 基于条件潜空间扩散模型，端到端生成可控三维多晶结构。
- 晶粒尺寸、球形度等属性控制精度 $R^2 > 0.972$，优于传统方法。
- 生成结构可通过晶体塑性有限元模拟验证，适合材料设计与优化。
多晶材料的三维（3D）微观结构对其力学与物理性能具有决定性影响。真实且可控地构建这些微观结构是揭示结构-性能关系的关键步骤，但仍是重大挑战。本文提出 PolyCrysDiff，一种基于条件潜空间扩散的框架，实现可计算3D多晶微观结构的端到端生成。全面的定性和定量评估表明，PolyCrysDiff 能准确再现目标晶粒形态、取向分布及三维空间相关性，在晶粒属性（如尺寸和球形度）控制上 $R^2$ 超过 0.972，显著优于主流的马尔可夫随机场（MRF）与卷积神经网络（CNN）方法。通过一系列晶体塑性有限元法（CPFEM）模拟验证了生成结构的可计算性与物理有效性。利用 PolyCrysDiff 的可控生成能力，系统揭示了晶级微观结构特征对多晶材料力学性能的影响。该成果有望推动多晶材料的加速数据驱动优化与设计。
原文摘要 · Abstract (English)
The three-dimensional (3D) microstructures of polycrystalline materials exert a critical influence on their mechanical and physical properties. Realistic, controllable construction of these microstructures is a key step toward elucidating structure-property relationships, yet remains a formidable challenge. Herein, we propose PolyCrysDiff, a framework based on conditional latent diffusion that enables the end-to-end generation of computable 3D polycrystalline microstructures. Comprehensive qualitative and quantitative evaluations demonstrate that PolyCrysDiff faithfully reproduces target grain morphologies, orientation distributions, and 3D spatial correlations, while achieving an $R^2$ over 0.972 on grain attributes (e.g., size and sphericity) control, thereby outperforming mainstream approaches such as Markov random field (MRF)- and convolutional neural network (CNN)-based methods. The computability and physical validity of the generated microstructures are verified through a series of crystal plasticity finite element method (CPFEM) simulations. Leveraging PolyCrysDiff's controllable generative capability, we systematically elucidate how grain-level microstructural characteristics affect the mechanical properties of polycrystalline materials. This development is expected to pave a key step toward accelerated, data-driven optimization and design of polycrystalline materials.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。