Dataset Information

Tackling Structural Complexity in Li₂S-P₂S₅ Solid-State Electrolytes Using Machine Learning Potentials.

ABSTRACT: The lithium thiophosphate (LPS) material class provides promising candidates for solid-state electrolytes (SSEs) in lithium ion batteries due to high lithium ion conductivities, non-critical elements, and low material cost. LPS materials are characterized by complex thiophosphate microchemistry and structural disorder influencing the material performance. To overcome the length and time scale restrictions of ab initio calculations to industrially applicable LPS materials, we develop a near-universal machine-learning interatomic potential for the LPS material class. The trained Gaussian Approximation Potential (GAP) can likewise describe crystal and glassy materials and different P-S connectivities PmSn. We apply the GAP surrogate model to probe lithium ion conductivity and the influence of thiophosphate subunits on the latter. The materials studied are crystals (modifications of Li3PS4 and Li7P3S11), and glasses of the xLi2S-(100 - x)P2S5 type (x = 67, 70 and 75). The obtained material properties are well aligned with experimental findings and we underscore the role of anion dynamics on lithium ion conductivity in glassy LPS. The GAP surrogate approach allows for a variety of extensions and transferability to other SSEs.

SUBMITTER: Staacke CG

PROVIDER: S-EPMC9458117 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Publications

Tackling Structural Complexity in Li2S-P2S5 Solid-State Electrolytes Using Machine Learning Potentials.

Staacke Carsten G CG Huss Tabea T Margraf Johannes T JT Reuter Karsten K Scheurer Christoph C

Nanomaterials (Basel, Switzerland) 20220826 17

The lithium thiophosphate (LPS) material class provides promising candidates for solid-state electrolytes (SSEs) in lithium ion batteries due to high lithium ion conductivities, non-critical elements, and low material cost. LPS materials are characterized by complex thiophosphate microchemistry and structural disorder influencing the material performance. To overcome the length and time scale restrictions of ab initio calculations to industrially applicable LPS materials, we develop a nea ...[more]

PMID: 36079988

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Project description:Several "Beyond Li-Ion Battery" concepts such as all solid-state batteries and hybrid liquid/solid systems envision the use of a solid electrolyte to protect Li-metal anodes. These configurations are very attractive due to the possibility of exceptionally high energy densities and high (dis)charge rates, but they are far from being realized practically due to a number of issues including high interfacial resistance and difficulties associated with fabrication. One of the most promising solid electrolyte systems for these applications is Al or Ga stabilized Li7La3Zr2O12 (LLZO) based on high ionic conductivities and apparent stability against reduction by Li metal. Nevertheless, the fabrication of dense LLZO membranes with high ionic conductivity and low interfacial resistances remains challenging; it definitely requires a better understanding of the structural and electrochemical properties. In this study, the phase transition from garnet (Ia3̅d, No. 230) to "non-garnet" (I4̅3d, No. 220) space group as a function of composition and the different sintering behavior of Ga and Al stabilized LLZO are identified as important factors in determining the electrochemical properties. The phase transition was located at an Al:Ga substitution ratio of 0.05:0.15 and is accompanied by a significant lowering of the activation energy for Li-ion transport to 0.26 eV. The phase transition combined with microstructural changes concomitant with an increase of the Ga/Al ratio continuously improves the Li-ion conductivity from 2.6 × 10-4 S cm-1 to 1.2 × 10-3 S cm-1, which is close to the calculated maximum for garnet-type materials. The increase in Ga content is also associated with better densification and smaller grains and is accompanied by a change in the area specific resistance (ASR) from 78 to 24 Ω cm2, the lowest reported value for LLZO so far. These results illustrate that understanding the structure-properties relationships in this class of materials allows practical obstacles to its utilization to be readily overcome.

Dataset Information

Tackling Structural Complexity in Li₂S-P₂S₅ Solid-State Electrolytes Using Machine Learning Potentials.

Publications

Tackling Structural Complexity in Li<sub>2</sub>S-P<sub>2</sub>S<sub>5</sub> Solid-State Electrolytes Using Machine Learning Potentials.

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