Multi4D：一种用于复杂材料界面结构确定的端到端神经网络
4DMulti: automated multicomponent identification at complex material interfaces
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中文总结 AI 辅助
Multi4D是一种基于物理信息的神经网络框架，利用4D-STEM数据自动识别复杂材料界面的多组分晶体结构，实现98.82%准确率的高保真结构图谱生成。
中文摘要 AI 辅助
异质界面决定了功能材料的性能与退化，因此将局部结构变化与宏观失效机制联系起来对于指导未来材料设计至关重要。然而，结构异质性、相重叠和局部无序会产生高度复杂的衍射特征，使得在原子分辨率下跨大视场解释扩展过渡区域变得困难。在此，我们引入了Multi4D，一种基于物理信息的神经网络框架，用于利用四维扫描透射电子显微镜（4D-STEM）进行自动化多组分晶体学识别。通过将用于物理约束风格转换的潜空间扩散Transformer与用于方向无关分类的旋转不变卷积神经网络相结合，该方法将多组分衍射数据集转换为确定性的晶体学图谱，准确率达98.82%。此外，我们引入了衍射推断结构复杂性，这是一种从分类器预测不确定性导出的信息论熵度量，用于量化局部结构模糊性。我们将Multi4D应用于生成复杂超导异质结构、腐蚀合金表面和退化固态电池界面的高保真结构图谱，空间分辨率低至单纳米。该框架为自动化显微镜建立了一种统计稳健的分析范式，促进了工业质量控制和界面设计原理的数据驱动发现。
英文摘要
Mapping crystalline phases at heterogeneous interfaces is essential for understanding material performance and degradation. However, structural heterogeneity, phase overlap, and local disorder complicate diffraction interpretation, while growing data volumes make manual analysis increasingly impractical. We introduce 4DMulti, a physics-guided learning framework for automated multicomponent identification from large-scale four-dimensional scanning transmission electron microscopy (4D-STEM) data. The supporting diffraction data resource comprises over 6 million high-quality experimental patterns and labeled patterns generated by Sim2real. A retrieval-conditioned latent diffusion transformer (Sim2real) translates simulated patterns into experimental-style examples under constraints designed to preserve Bragg geometry, while a rotation-invariant coordinate convolutional network identifies phases across in-plane rotations. 4DMulti achieves 98.82% classification accuracy on a five-phase experimental nanoparticle benchmark, with ablation studies supporting the complementary benefits of domain adaptation and rotation-invariant classification. We define diffraction-inferred structural complexity (DISC), a normalized predictive entropy score that quantifies phase-assignment ambiguity within a specified candidate phase library. We apply 4DMulti to generate structural maps of superconducting heterostructures, corroded alloy surfaces, and degraded solid-state battery interfaces down to single-nanometer spatial resolution. 4DMulti connects simulation-derived crystallographic knowledge to automated experimental interpretation, establishing a foundation for scalable analysis of complex interfaces and data-driven discovery of interfacial design principles.
发表机构
- Global Institute of Future Technology, Shanghai Jiao Tong University（上海交通大学未来技术学院）
- Global College, Shanghai Jiao Tong University（上海交通大学国际学院）
- School of Materials Science and Engineering, Shanghai Jiao Tong University（上海交通大学材料科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。
