Oxidation states of materials are characterized by the X-ray absorption near edge structure (XANES) region in X-ray absorption spectroscopy (XAS). However, the challenges in identifying oxide states are strong depending on the researcher’s judgment based on shift changes between measured XAS and reference spectra data. Here, automatic oxidation threshold recognition is performed using machine learning and experimental XAS spectra. In particular, a workflow from experimental data collection, data preprocessing and prediction using machine learning are proposed. 10 descriptors for recognizing the oxide state in XAS spectra are discovered. More importantly, the oxide states of unknown experimental XAS spectra are identified using a trained machine. The proposed approach thus allows for the machine learning to automatically recognize the oxidation threshold of a given XAS spectra without the presence of reference data, leading to the fast analysis of XAS spectra.

中文翻译：

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使用监督式机器学习自动识别XAFS数据的氧化阈值†

材料的氧化态由X射线吸收光谱（XAS）中的X射线吸收在边缘结构（XANES）区域表示。但是，根据研究人员基于测得的XAS与参考光谱数据之间的偏移变化做出的判断，识别氧化物状态面临着巨大的挑战。在这里，使用机器学习和实验XAS光谱执行自动氧化阈值识别。特别地，提出了来自使用机器学习的实验数据收集，数据预处理和预测的工作流。发现了10个用于识别XAS光谱中的氧化物状态的描述符。更重要的是，使用训练有素的机器可以识别未知实验XAS光谱的氧化物状态。