Electron energy loss spectroscopy (EELS) and X-ray absorption spectroscopy (XAS) provide detailed information about bonding, distributions and locations of atoms, and their coordination numbers and oxidation states. However, analysis of XAS/EELS data often relies on matching an unknown experimental sample to a series of simulated or experimental standard samples. This limits analysis throughput and the ability to extract quantitative information from a sample. In this work, we have trained a random forest model capable of predicting the oxidation state of copper based on its L-edge spectrum. Our model attains an R² score of 0.85 and a root mean square error of 0.24 on simulated data. It has also successfully predicted experimental L-edge EELS spectra taken in this work and XAS spectra extracted from the literature. We further demonstrate the utility of this model by predicting simulated and experimental spectra of mixed valence samples generated by this work. This model can be integrated into a real-time EELS/XAS analysis pipeline on mixtures of copper-containing materials of unknown composition and oxidation state. By expanding the training data, this methodology can be extended to data-driven spectral analysis of a broad range of materials.

电子能量损失光谱 (EELS) 和 X 射线吸收光谱 (XAS) 提供有关原子的键合、分布和位置及其配位数和氧化态的详细信息。然而，XAS/EELS 数据的分析通常依赖于将未知实验样品与一系列模拟或实验标准样品进行匹配。这限制了分析吞吐量和从样品中提取定量信息的能力。在这项工作中，我们训练了一个随机森林模型，能够根据铜的 L 边光谱预测铜的氧化态。我们的模型在模拟数据上获得了 0.85 的R ²分数和 0.24 的均方根误差。它还成功预测了本工作中采用的实验 L 边缘 EELS 光谱以及从文献中提取的 XAS 光谱。我们通过预测本工作生成的混合价样本的模拟和实验光谱进一步证明了该模型的实用性。该模型可以集成到对未知成分和氧化态的含铜材料混合物的实时 EELS/XAS 分析管道中。通过扩展训练数据，该方法可以扩展到多种材料的数据驱动光谱分析。