Four-dimensional scanning transmission electron microscopy, coupled with a wide array of data analytics, has unveiled new insights into complex materials. Here, we introduce a straightforward unsupervised machine learning approach that entails dimensionality reduction and clustering with minimal hyperparameter tuning to semi-automatically identify unique coexisting structures in metallic alloys. Applying cepstral transformation to the original diffraction dataset improves this process by effectively isolating phase information from potential signal ambiguity caused by sample tilt and thickness variations, commonly observed in electron diffraction patterns. In a case study of a NiTiHfAl shape memory alloy, conventional scanning transmission electron microscopy imaging struggles to accurately identify a low-contrast precipitate at lower magnifications, posing challenges for microscale analyses. We find that our method efficiently separates multiple coherent structures while using objective means of determining hyperparameters. Furthermore, we demonstrate how the clustering result facilitates more robust strain mapping to provide immediate and quantitative structural insights.

四维扫描透射电子显微镜与广泛的数据分析相结合，揭示了对复杂材料的新见解。在这里，我们介绍了一种简单的无监督机器学习方法，该方法需要通过最小的超参数调整进行降维和聚类，以半自动识别金属合金中独特的共存结构。将倒谱变换应用于原始衍射数据集，可以有效地将相位信息与由样品倾斜和厚度变化引起的潜在信号模糊性（通常在电子衍射图案中观察到）隔离开来，从而改进了这一过程。在 NiTiHfAl 形状记忆合金的案例研究中，传统的扫描透射电子显微镜成像难以在较低放大倍数下准确识别低对比度沉淀物，这给微观分析带来了挑战。我们发现我们的方法有效地分离了多个相干结构，同时使用确定超参数的客观方法。此外，我们还演示了聚类结果如何促进更强大的应变映射，以提供即时和定量的结构见解。