Despite 2D materials holding great promise for a broad range of applications, the proliferation of devices and their fulfillment of real-life demands are still far from being realized. Experimentally obtainable samples commonly experience a wide range of perturbations (ripples and wrinkles, point and line defects, grain boundaries, strain field, doping, water intercalation, oxidation, edge reconstructions) significantly deviating the properties from idealistic models. These perturbations, in general, can be entangled or occur in groups with each group forming a complex perturbation making the interpretations of observable physical properties and the disentanglement of simultaneously acting effects a highly non-trivial task even for an experienced researcher. Here we generalise statistical correlation analysis of excitonic spectra of monolayer WS₂, acquired by hyperspectral absorption and photoluminescence imaging, to a multidimensional case, and examine multidimensional correlations via unsupervised machine learning algorithms. Using principal component analysis we are able to identify four dominant components that are correlated with tensile strain, disorder induced by adsorption or intercalation of environmental molecules, multi-layer regions and charge doping, respectively. This approach has the potential to determine the local environment of WS₂ monolayers or other 2D materials from simple optical measurements, and paves the way toward advanced, machine-aided, characterization of monolayer matter.

尽管2D材料在广泛的应用领域中具有广阔的前景，但设备的普及及其对现实生活的满足仍远未实现。可通过实验获得的样品通常会经历各种各样的扰动（波纹和皱纹，点和线缺陷，晶界，应变场，掺杂，水嵌入，氧化，边缘重建），从而使特性与理想模型发生明显偏离。通常，这些扰动可以纠缠在一起或成组发生，每组形成一个复杂的扰动，使得对于可观察到的物理特性的解释和同时作用的解开，即使对于有经验的研究人员而言，也是一项极为艰巨的任务。在这里，我们概括了单层WS的激子谱的统计相关分析通过高光谱吸收和光致发光成像获得的图_2所示为多维情况，并通过无监督的机器学习算法检查多维相关性。使用主成分分析，我们能够识别出四个主要成分，分别与拉伸应变，由环境分子的吸附或嵌入引起的无序，多层区域和电荷掺杂有关。这种方法具有通过简单的光学测量确定WS ₂单层或其他2D材料的局部环境的潜力，并为单层物质的高级机器辅助表征铺平了道路。