Insights into Lithium Sulfide Glass Electrolyte Structures and Ionic Conductivity via Machine Learning Force Field Simulations
Тип публикации: Journal Article
Дата публикации: 2024-04-03
SCImago Q1
Tоп 10% SCImago
WOS Q1
БС1
SJR: 1.614
CiteScore: 13.3
Impact factor: 7.8
ISSN: 19448244, 19448252
PubMed ID:
38568163
General Materials Science
Краткое описание
Sulfide-based solid electrolytes (SEs) are important for advancing all-solid-state batteries (ASSBs), primarily due to their high ionic conductivities and robust mechanical stability. Glassy SEs (GSEs) comprising mixed Si and P glass formers are particularly promising for their synthesis process and their ability to prevent lithium dendrite growth. However, to date, the complexity of their glassy structures hinders a complete understanding of the relationships between their structures and properties. This study introduces a new machine learning force field (ML-FF) tailored for lithium sulfide-based GSEs, enabling the exploration of their structural characteristics, mechanical properties, and lithium ionic conductivities. Using molecular dynamic (MD) simulations with this ML-FF, we explore the glass structures in varying compositions, including binary Li2S–SiS2 and Li2S–P2S5 as well as ternary Li2S–SiS2–P2S5. Our simulations yielded consistent results in terms of density, elastic modulus, radial distribution functions, and neutron structure factors compared to DFT and experimental work. Our findings reveal distinct local environments for Si and P within these glasses, with most Si atoms in edge-sharing configurations in Li2S–SiS2 and a mix of corner- and edge-sharing tetrahedra in the ternary Li2S–SiS2–P2S5 composition. For lithium ionic conductivity at 300 K, the 50Li2S–50SiS2 glass displayed the lowest conductivity at 2.1 mS/cm, while the 75Li2S–25P2S5 composition exhibited the highest conductivity at 3.6 mS/cm. The ternary glass showed a conductivity of 2.6 mS/cm, sitting between the two. Moreover, an in-depth analysis of lithium ion diffusion over the MD trajectory in the ternary glass demonstrated a significant correlation between diffusion pathways and the rotational dynamics of nearby SiS4 or PS4 tetrahedra. The ML-FF developed in this study provides an important tool for exploring a broad spectrum of solid-state and mixed former sulfide-based electrolytes.
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Zhou R. et al. Insights into Lithium Sulfide Glass Electrolyte Structures and Ionic Conductivity via Machine Learning Force Field Simulations // ACS applied materials & interfaces. 2024. Vol. 16. No. 15. pp. 18874-18887.
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Zhou R., Luo K., Martin S. W., An Q. Insights into Lithium Sulfide Glass Electrolyte Structures and Ionic Conductivity via Machine Learning Force Field Simulations // ACS applied materials & interfaces. 2024. Vol. 16. No. 15. pp. 18874-18887.
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TY - JOUR
DO - 10.1021/acsami.4c00618
UR - https://pubs.acs.org/doi/10.1021/acsami.4c00618
TI - Insights into Lithium Sulfide Glass Electrolyte Structures and Ionic Conductivity via Machine Learning Force Field Simulations
T2 - ACS applied materials & interfaces
AU - Zhou, Rui
AU - Luo, Kun
AU - Martin, Steve W
AU - An, Qi
PY - 2024
DA - 2024/04/03
PB - American Chemical Society (ACS)
SP - 18874-18887
IS - 15
VL - 16
PMID - 38568163
SN - 1944-8244
SN - 1944-8252
ER -
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@article{2024_Zhou,
author = {Rui Zhou and Kun Luo and Steve W Martin and Qi An},
title = {Insights into Lithium Sulfide Glass Electrolyte Structures and Ionic Conductivity via Machine Learning Force Field Simulations},
journal = {ACS applied materials & interfaces},
year = {2024},
volume = {16},
publisher = {American Chemical Society (ACS)},
month = {apr},
url = {https://pubs.acs.org/doi/10.1021/acsami.4c00618},
number = {15},
pages = {18874--18887},
doi = {10.1021/acsami.4c00618}
}
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MLA
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Zhou, Rui, et al. “Insights into Lithium Sulfide Glass Electrolyte Structures and Ionic Conductivity via Machine Learning Force Field Simulations.” ACS applied materials & interfaces, vol. 16, no. 15, Apr. 2024, pp. 18874-18887. https://pubs.acs.org/doi/10.1021/acsami.4c00618.
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