Download file (3.39MB)
Download as zip (3.07MB)

コレクション

引用

説明:

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.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: Machine learning, ELNES, XANES, PDOS

刊行年月日: 2024-08-15

出版者: American Chemical Society (ACS)

掲載誌:

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

研究助成金:

• 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

原稿種別: 出版者版 (Version of record)

MDR DOI:

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

関連資料:

その他の識別子:

連絡先:

更新時刻: 2024-10-02 12:30:19 +0900

MDRでの公開時刻: 2024-10-02 12:30:19 +0900