基于卷积神经网络的俄歇电子能谱与芯电子结合能的位点选择性官能团分类

中文摘要 AI 辅助

X射线光谱技术以原子位点特异性探测系统中的化学状态；然而，对新研究材料中局域键合环境的位点选择性表征通常依赖于参考光谱、多种互补光谱技术以及电子结构计算的组合。俄歇电子能谱长期以来作为X射线光电子能谱的第二种模态，用于解析具有重叠芯电子结合能的化学状态。本文表明，俄歇谱中编码的丰富信息提供了直接利用俄歇谱线形训练卷积神经网络，对有机分子进行位点选择性官能团分类的机会。此外，将芯电子结合能作为附加模态纳入，可改善分类性能，具体方式有两种：一是将拟合强度输入与结合能拼接，二是通过特征级线性调制层以结合能为条件进行分类。后一种方法先前是为图像分类中的视觉推理而开发的，本研究结果表明，这是纳入结合能的更稳健方法。这项工作展示了在信息丰富但难以解释的光谱技术中实现新的数据驱动表征能力的潜力。

英文摘要

X-ray spectroscopy techniques probe chemical states in systems with atom-site specificity; however, the site-selective characterization of local bond environments in novel research materials often relies on a combination of reference spectra, multiple complementary spectroscopic techniques and electronic structure calculations. Auger-electron spectroscopy has long accompanied x-ray photoelectron spectroscopy as a second modality to resolve chemical states with overlapping core-electron binding energies. Here I show that the wealth of information encoded in the Auger spectrum offers the opportunity to train convolutional neural networks for site-selective functional group classification in organic molecules directly from the Auger spectrum lineshape. Furthermore, the inclusion of the core-electron binding energy as an additional modality improves the classification, either by augmenting the input fitted intensities with the binding energy or by conditioning the classification on the binding energy via feature-wise linear modulation layers. The latter approach was previously developed for visual reasoning in image classification, and the present results show that this is the more robust approach for including the binding energy. This work demonstrates the potential for new data-driven characterization capabilities in spectroscopic techniques that are information-rich but difficult to interpret.

发表机构

• Data Science Institute, The University of Chicago（芝加哥大学数据科学研究所）

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