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Automatic oxidation threshold recognition of XAFS data using supervised machine learning

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Abstract

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.

Graphical abstract: Automatic oxidation threshold recognition of XAFS data using supervised machine learning

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Publication details

The article was received on 01 Apr 2019, accepted on 13 Jun 2019 and first published on 13 Jun 2019


Article type: Paper
DOI: 10.1039/C9ME00043G
Mol. Syst. Des. Eng., 2019, Advance Article

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    Automatic oxidation threshold recognition of XAFS data using supervised machine learning

    I. Miyazato, L. Takahashi and K. Takahashi, Mol. Syst. Des. Eng., 2019, Advance Article , DOI: 10.1039/C9ME00043G

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