Core-loss EELS dataset and neural networks for element identification

We present a large dataset containing simulated core-loss electron energy loss spectroscopy (EELS) spectra with the elemental content as ground-truth labels. Additionally we present some neural networks trained on this data for element identification.  The simulated dataset contains zero padded core-loss spectra from 0 to 3072 eV, which represents 107 core-loss edges through all 80 elements from Be up to Bi. The core-loss edges are calculated from the generalised oscillator strength (GOS) database presented by Zhang et al.[1] Generic fine structures using lifetime broadened peaks are used to imitate fine structure due to solid-state effects in experimental spectra. Generic low-loss regions are used to imitate the effect of multiple scattering. Each spectrum contains at least one edge of a given query element and possibly additional edges depending on samples drawn from The Materials Project [2]. The dataset contains for each of the 80 elements: 7000 training spectra, 1500 test spectra, 600 validation spectra and 100 spectra representing only the query element. This results in a total 736 000 labeled spectra. Code on how to  - read the simulated data - transform HDF5 format to TFRecord format - train and evaluate neural networks using the simulated data - use the trained networks for automated element identification is available on GitHub at arnoannys/EELS_ID A full report on the simulation of the dataset and the training and evaluation of the neural networks can be found at:                    Annys, A., Jannis, D. & Verbeeck, J. Deep learning for automated materials characterisation in core-loss electron energy loss spectroscopy. Sci Rep 13, 13724 (2023). https://doi.org/10.1038/s41598-023-40943-7 [1] Zezhong Zhang, Ivan Lobato, Daen Jannis, Johan Verbeeck, Sandra Van Aert, & Peter Nellist. (2023). Generalised oscillator strength for core-shell electron excitation by fast electrons based on Dirac solutions (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7729585 [2] Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, Kristin A. Persson; Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. __APL Mater__ 1 July 2013; 1 (1): 011002. [https://doi.org/10.1063/1.4812323](https://doi.org/10.1063/1.4812323)

本报告呈现了一项包含模拟核损失电子能量损失光谱（EELS）数据的大型数据集，其中元素含量作为真实标签。此外，我们还展示了针对此数据集训练的一些用于元素识别的神经网络。该模拟数据集包含了从0至3072 eV的零填充核损失光谱，代表从铍至铋的80种元素的所有107个核损失边缘。核损失边缘的计算基于张等人提供的广义振荡强度（GOS）数据库[1]。采用寿命展宽峰的通用精细结构来模拟实验光谱中的固态效应引起的精细结构。使用通用低损失区域来模拟多重散射效应。每个光谱至少包含一个查询元素的边缘，并可能包含来自材料项目的样本的额外边缘。对于80种元素中的每一种，数据集包含7000个训练光谱、1500个测试光谱、600个验证光谱以及仅代表查询元素的100个光谱。这总计达到了736,000个标记光谱。有关如何读取模拟数据、将HDF5格式转换为TFRecord格式、使用模拟数据进行神经网络训练和评估，以及使用训练好的网络进行自动化元素识别的代码，可在GitHub的arnoannys/EELS_ID上找到。关于数据集的模拟、神经网络的训练和评估的详细报告，可在Annys, A., Jannis, D. & Verbeeck, J.所著的《深度学习在核损失电子能量损失光谱中自动材料表征》一文中查阅，该文发表于Sci Rep 13, 13724 (2023)。[1] Zezhong Zhang, Ivan Lobato, Daen Jannis, Johan Verbeeck, Sandra Van Aert, & Peter Nellist. (2023). 基于Dirac解的核壳电子激发的广义振荡强度（1.0）[数据集]. Zenodo. https://doi.org/10.5281/zenodo.7729585 [2] Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, Kristin A. Persson; 评论：材料项目：加速材料创新的材料基因组方法。__APL Mater__ 2013年7月1日; 1 (1): 011002. [https://doi.org/10.1063/1.4812323](https://doi.org/10.1063/1.4812323)