Department of Materials Science and Engineering, McMaster University, 1280 Main Street West, Hamilton, ON, L9H 4L7, Canada.
Canadian Center for Electron Microscopy, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4M1, Canada.
Sci Rep. 2019 Feb 14;9(1):2126. doi: 10.1038/s41598-019-38482-1.
The interaction between matter and electromagnetic radiation provides a rich understanding of what the matter is composed of and how it can be quantified using spectrometers. In many cases, however, the calibration of the spectrometer changes as a function of time (such as in electron spectrometers), or the absolute calibration may be different between different instruments. Calibration differences cause difficulties in comparing the absolute position of measured emission or absorption peaks between different instruments and even different measurements taken at different times on the same instrument. Present methods of avoiding this issue involve manual feature extraction of the original signal or qualitative analysis. Here we propose automated feature extraction using deep convolutional neural networks to determine the class of compound given only the shape of the spectrum. We classify three unique electronic environments of manganese (being relevant to many battery materials applications) in electron energy loss spectroscopy using 2001 spectra we collected in addition to testing on spectra from different instruments. We test a variety of commonly used neural network architectures found in the literature and propose a new fully convolutional architecture with improved translation-invariance which is immune to calibration differences.
物质与电磁辐射的相互作用为我们提供了丰富的认识，了解物质由什么组成，以及如何使用光谱仪对其进行定量。然而，在许多情况下，光谱仪的校准会随时间变化（例如在电子能谱仪中），或者不同仪器之间的绝对校准可能不同。校准差异导致在比较不同仪器之间测量的发射或吸收峰的绝对位置，甚至在同一仪器上不同时间进行的不同测量时遇到困难。目前避免这个问题的方法包括对原始信号进行手动特征提取或定性分析。在这里，我们提出了一种使用深度卷积神经网络进行自动特征提取的方法，仅根据光谱的形状即可确定化合物的类别。我们使用除了来自不同仪器的光谱之外还收集了 2001 个光谱，在电子能量损失光谱学中对锰的三种独特电子环境（与许多电池材料应用相关）进行分类。我们测试了文献中常用的各种神经网络架构，并提出了一种具有改进平移不变性的全新全卷积架构，该架构对校准差异具有免疫力。
