The key objective of scanning probe microscopy (SPM) techniques is the optimal representation of the nanoscale surface structure and functionality inferred from the dynamics of the cantilever. This is particularly pertinent today, as the SPM community has seen a rapidly growing trend towards simultaneous capture of multiple imaging channels and complex modes of operation involving high-dimensional information-rich datasets, bringing forward the challenges of visualization and analysis, particularly for cases where the underlying dynamic model is poorly understood. To meet this challenge, we present a data-driven approach, Graph-Bootstrapping, based on low-dimensional manifold learning of the full SPM spectra and demonstrate its successes for high-veracity mechanical mapping on a mixed polymer thin film and resolving irregular hydration structure of calcite at atomic resolution. Using the proposed methodology, we can efficiently reveal and hierarchically represent salient material features with rich local details, further enabling denoising, classification, and high-resolution functional imaging.

中文翻译：

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通过Graph-Bootstrapping在扫描探针显微镜中进行高功能功能成像。

扫描探针显微镜（SPM）技术的主要目标是从悬臂的动力学推断出的纳米级表面结构和功能的最佳表示。这在今天尤为重要，因为SPM社区看到了同时捕获多个成像通道和涉及高维信息丰富数据集的复杂操作模式的迅速增长的趋势，这带来了可视化和分析的挑战，特别是对于基本的动态模型了解甚少。为迎接这一挑战，我们提出了一种数据驱动的方法，图引导程序，基于完整SPM光谱的低维流形学习，并展示了其在混合聚合物薄膜上进行高精确度机械制图并以原子分辨率解​​决方解石不规则水合结构的成功经验。使用提出的方法，我们可以有效地揭示和分层显示具有丰富局部细节的显着材料特征，从而进一步实现降噪，分类和高分辨率功能成像。