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Tackling Structural Complexity in Li2S-P2S5 Solid-State Electrolytes Using Machine Learning Potentials
Carsten G Staacke1, Tabea Huss1, Johannes T Margraf1
1Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin, Germany.
Nanomaterials (Basel, Switzerland)
|September 9, 2022
Summary
We developed a machine-learning potential for lithium thiophosphate solid-state electrolytes, enabling efficient simulation of ion conductivity. This approach accurately predicts material properties and reveals the impact of anion dynamics on performance.
Area of Science:
- Materials Science
- Computational Chemistry
- Electrochemistry
Background:
- Lithium thiophosphate (LPS) materials are promising solid-state electrolytes (SSEs) for lithium-ion batteries, offering high ionic conductivity and cost-effectiveness.
- The performance of LPS materials is significantly influenced by their complex microchemistry and structural disorder.
- Traditional ab initio calculations face limitations in simulating industrially relevant LPS materials due to time and length scale constraints.
Purpose of the Study:
- To develop a versatile machine-learning interatomic potential for the lithium thiophosphate (LPS) material class.
- To overcome computational limitations for simulating LPS materials at industrially relevant scales.
- To investigate the influence of thiophosphate subunits on lithium ion conductivity in LPS.
Main Methods:
- Development and training of a Gaussian Approximation Potential (GAP) for LPS materials, capable of describing both crystalline and glassy states.
- Application of the GAP surrogate model to simulate lithium ion conductivity in various LPS compositions (Li3PS4, Li7P3S11, and xLi2S-(100-x)P2S5 glasses).
- Analysis of thiophosphate subunit structures and anion dynamics to understand their effect on ionic transport.
Main Results:
- The trained GAP accurately reproduces material properties, aligning well with experimental findings.
- Simulations revealed the critical role of anion dynamics in determining lithium ion conductivity, particularly in glassy LPS.
- The machine-learning potential demonstrates transferability to other solid-state electrolyte materials.
Conclusions:
- The developed machine-learning interatomic potential provides an efficient and accurate tool for studying LPS materials.
- This approach enables exploration of structure-property relationships crucial for designing advanced solid-state electrolytes.
- The GAP methodology offers a scalable pathway for accelerating the discovery and optimization of next-generation battery materials.
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