Discovering virtual Na-based argyrodites as solid-state electrolytes using DFT, AIMD, and machine learning techniques
In light of the current absence of experimentally synthesized Na-based argyrodites, we systematically investigated a large compositional space comprising 4375 hypothetical Na-based argyrodite structures through DFT and AIMD calculations.
Authors
- Byung Do Lee (ORCID: https://orcid.org/0000-0002-1660-7339)
- Kee‐Sun Sohn (ORCID: https://orcid.org/0000-0002-7496-2283)
- Woon Bae Park (ORCID: https://orcid.org/0000-0002-0631-1905)
- Young‐Kook Lee (ORCID: https://orcid.org/0000-0001-9866-5147)
- Min Young Cho
- Hee-jeong Kim (ORCID: https://orcid.org/0000-0003-4558-7936)
- Seonghwan Kim (ORCID: https://orcid.org/0000-0001-7735-3582)
- Deepak S. Gavali
- Kyunglim Pyo
Institutions
- Yonsei University (KR)
- Sunchon National University (KR)
- Sejong University (KR)
Publication Details
- Journal
- Journal of Materials Chemistry A
- Published
- 2024-11-18
- DOI
- https://doi.org/10.1039/d4ta06927g
- Citations
- 8
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.94