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Mechanistic insights into superionic thioarsenate argyrodite solid electrolytes via machine learning interatomic potentials
- Jang, Myeongcho;
- Park, Kanguk;
- Lee, Yongheum;
- Shim, Joon Hyung;
- Kim, Kwangnam;
- 외 1명
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1초록
The lithium argyrodite sulfide solid electrolyte Li6PS5Cl has attracted considerable interest for all-solid-state batteries owing to its high ionic conductivity, which can be further enhanced through ionic substitution. Although a variety of substitutions have been investigated, thioarsenate argyrodites remain comparatively underexplored. Here, we systematically investigate the phase stability and Li-ion conduction mechanisms in superionic Br-incorporated thioarsenate argyrodites using first-principles calculations and molecular dynamics simulations based on machine learning interatomic potentials (MLIPs). Systematic variation of S/Br site inversion reveals that an optimal degree of anion disorder significantly enhances inter-cage connectivity and facilitates long-range Li-ion diffusion. Configurational entropy serves as an effective quantitative descriptor of anion disorder, exhibiting a strong correlation with ionic conductivity. While greater anion disorder induced by site inversion and higher Br content enhances ionic conductivity up to 50 mS cm-1, it simultaneously reduces structural stability. This trade-off results in an optimal window in which a moderate level of disorder yields conductivities exceeding 20 mS cm-1 while maintaining synthetic feasibility. This work highlights the reliability and efficiency of MLIPs for elucidating ion-transport mechanisms and accelerating the design of novel superionic argyrodites.
키워드
- 제목
- Mechanistic insights into superionic thioarsenate argyrodite solid electrolytes via machine learning interatomic potentials
- 저자
- Jang, Myeongcho; Park, Kanguk; Lee, Yongheum; Shim, Joon Hyung; Kim, Kwangnam; Yu, Seungho
- 발행일
- 2025-10-07
- 유형
- Article
- 권
- 13
- 호
- 39
- 페이지
- 33203 ~ 33211