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  • Insights into Lithium Sulfide Glass Electrolyte Structures and Ionic Conductivity via Machine Learning Force Field Simulations
Title: Insights into Lithium Sulfide Glass Electrolyte Structures and Ionic Conductivity via Machine Learning Force Field Simulations
Sulfide-based solid electrolytes (SEs) are emerging as compelling materials for all-solid-state batteries (ASSBs), primarily due to their high ionic conductivities and robust mechanical stability. In particular, glassy SEs (GSEs) comprising mixed Si and P glassformers show promise, thanks to their efficient synthesis process and their intrinsic 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. Here, new machine learning force field (ML- FF) specifically designed for lithium sulfide-based GSEs has been developed. This ML-FF has been used to investigate the structural characteristics, mechanical properties, and lithium ionic conductivities in binary lithium thiosilicate and lithium thiophosphate GSEs, as well as their ternary mixed glassformer (MGF) lithium thiosilicophosphate GSEs. Molecular dynamic (MD) simulations using the ML-FF were conducted to explore the glass structures in varying compositions, including binary Li2S-SiS2 and Li2S-P2S5, as well as ternary Li2S-SiS2-P2S5. The simulations with the ML-FF yielded consistent results in terms of density, elastic modulus, radial distribution functions, and neutron structure factors, compared to DFT and experimental work. A key focus of this study was to investigate the local environments of Si and P molecular clusters. We discovered that most Si atoms in the Li2S-SiS2 GSE are situated in an edge-sharing environment, while the Li2S-P2S5 glass contained a minor proportion of edge-sharing P2S62- environments. In the ternary 60Li2S-32SiS2-8P2S5 glass, the ML-FF predicted similar P environments as observed in the binary Li2S-P2S5 glass. Additionally, it indicated the coexistence of corner and edge-sharing between PS4 and SiS4 tetrahedra in this ternary composition. Concerning lithium ionic conductivity at 300K, all studied glass compositions exhibited similar magnitudes and followed the Arrhenius relationship. The 50Li2S-50SiS2 glass displayed the lowest conductivity at 2.1 mS/cm, while the 75Li2S-25P2S5 composition exhibited the highest at 3.6 2 mS/cm. The ternary glass showed a conductivity of 2.57 mS/cm, sitting between the two. Interestingly, the predicted conductivities were about an order of magnitude higher than experimental values for the binary glasses but aligning more closely with that of the ternary glass. 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 shows immense potential as a versatile tool for exploring a broad spectrum of solid-state and mixed-former sulfide-based electrolytes.  more » « less
Award ID(s):
1936913
PAR ID:
10591428
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
ACS Applied Materials and Interfaces
Date Published:
Journal Name:
ACS Applied Materials & Interfaces
Volume:
16
Issue:
15
ISSN:
1944-8244
Page Range / eLocation ID:
18874 to 18887
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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