用机器学习分析电子显微数据，揭示玻璃态材料的纳米级化学结构差异。
Protocol for Clustering 4DSTEM Data for Phase Differentiation in Glasses
- 通过主成分分析与t-SNE/UMAP降维后聚类，从4D-STEM数据中识别出4类不同组分区域。
- 各聚类对应明确化学特征：如锗、碲、锑富集区及氧掺杂区，且结构差异显著。
- 适合材料表征、相变机制研究者，尤其关注纳米尺度异质性的团队。
相变材料（如Ge-Sb-Te合金）因其在非挥发性存储中的快速可逆相变而广泛应用，但其功能特性受纳米尺度化学与结构差异强烈影响，传统方法难以解析。本文将无监督机器学习应用于4维扫描透射电镜（4D-STEM）数据，对Ge-Sb-Te材料进行相区分。经主成分分析（PCA）预处理与降维后，利用t-SNE和UMAP进行聚类验证，并通过轮廓系数优化k-means聚类，成功识别出4个显著簇。这些簇映射回衍射数据后，元素强度直方图显示各簇具有独特化学特征：簇1富含氧和锗，簇2富集碲，簇3含锑，簇4再次富锗。平均衍射图谱进一步证实结构差异。结果表明，聚类分析可有效关联局部化学与结构特征，为理解相变材料内在异质性提供有力框架。
原文摘要 · Abstract (English)
Phase-change materials (PCMs) such as Ge-Sb-Te alloys are widely used in non-volatile memory applications due to their rapid and reversible switching between amorphous and crystalline states. However, their functional properties are strongly governed by nanoscale variations in composition and structure, which are challenging to resolve using conventional techniques. Here, we apply unsupervised machine learning to 4-dimensional scanning transmission electron microscopy (4D-STEM) data to identify compositional and structural heterogeneity in Ge-Sb-Te. After preprocessing and dimensionality reduction with principal component analysis (PCA), cluster validation was performed with t-SNE and UMAP, followed by k-means clustering optimized through silhouette scoring. Four distinct clusters were identified which were mapped back to the diffraction data. Elemental intensity histograms revealed chemical signatures change across clusters, oxygen and germanium enrichment in Cluster 1, tellurium in Cluster 2, antimony in Cluster 3, and germanium again in Cluster 4. Furthermore, averaged diffraction patterns from these clusters confirmed structural variations. Together, these findings demonstrate that clustering analysis can provide a powerful framework for correlating local chemical and structural features in PCMs, offering deeper insights into their intrinsic heterogeneity.
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