论文标题
通过机器学习来绘制电子能量损失光谱的低损失区域
Charting the low-loss region in Electron Energy Loss Spectroscopy with machine learning
论文作者
论文摘要
利用电子损坏光谱（EEL）提供的信息需要可靠的访问低损失区域，其中零损坏峰（ZLP）通常淹没了与试样无弹性散射相关的贡献。在这里，我们在粒子物理学中开发了机器学习技术，以实现与模型无关的，多维的确定，并具有忠实的不确定性估计。然后，将这种新颖的方法用于减去以2H/3R混合多型的特征的花样WS $ _2 $纳米结构中获得的EEL光谱的ZLP。从结果的减法光谱中，我们确定多型ws $ _2 $的带隙的性质和价值，找到$ e _ {\ rm bg} = 1.6 _ { - 0.2}^{+0.3} \，{\ rm ev} $，并清楚地preferce prece prece prece prece prece prece preceprect bandgap。此外，我们证明了这种方法如何使我们能够稳健地识别出对较小能量损失的激子过渡。我们的方法已经实施并在称为Eelsfitter的开源Python软件包中提供。
Exploiting the information provided by electron energy-loss spectroscopy (EELS) requires reliable access to the low-loss region where the zero-loss peak (ZLP) often overwhelms the contributions associated to inelastic scatterings off the specimen. Here we deploy machine learning techniques developed in particle physics to realise a model-independent, multidimensional determination of the ZLP with a faithful uncertainty estimate. This novel method is then applied to subtract the ZLP for EEL spectra acquired in flower-like WS$_2$ nanostructures characterised by a 2H/3R mixed polytypism. From the resulting subtracted spectra we determine the nature and value of the bandgap of polytypic WS$_2$, finding $E_{\rm BG} = 1.6_{-0.2}^{+0.3}\,{\rm eV}$ with a clear preference for an indirect bandgap. Further, we demonstrate how this method enables us to robustly identify excitonic transitions down to very small energy losses. Our approach has been implemented and made available in an open source Python package dubbed EELSfitter.
