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Local electronic structure in the ground state is essential for understanding the stability and properties of materials. Core-loss spectroscopy using electron or X-ray provides insights into the local electronic structure, but directly observable information is limited to the partial density of state (PDOS) of the conduction band at the excited state. To overcome this limitation, we developed a machine learning (ML) approach by creating a database of Si-K core-loss spectra and corresponding ground-state PDOS for various silicon structures. Using this database, we constructed an ML model capable of predicting the atom-specific ground-state PDOS of the valence and conduction bands from Si-K edges. Our model demonstrated the ability of the ML to extract the complex correlation between ground-state PDOS and Si-K edges. This study provides crucial insights into achieving atomic-level analysis of ground-state electronic structures, paving the way for a deeper understanding of stability and properties of materials.

Rights:

• Creative Commons BY Attribution 4.0 International
  

Keyword: Machine learning, ELNES, XANES, PDOS

Date published: 2024-08-15

Publisher: American Chemical Society (ACS)

Journal:

• The Journal of Physical Chemistry C (ISSN: 19327447) vol. 128 issue. 32 p. 13500-13507

Funding:

• New Energy and Industrial Technology Development Organization
• Core Research for Evolutional Science and Technology JP-MJCR1993
• Japan Society for the Promotion of Science 24K08016
• Japan Society for the Promotion of Science 24KJ0939
• Japan Society for the Promotion of Science JP19H05787

Manuscript type: Publisher's version (Version of record)

MDR DOI:

First published URL: https://doi.org/10.1021/acs.jpcc.4c02818

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Updated at: 2024-10-02 12:30:19 +0900

Published on MDR: 2024-10-02 12:30:19 +0900