Regensburg 2022 – wissenschaftliches Programm
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MM: Fachverband Metall- und Materialphysik
MM 2: Computational Materials Modelling: Energy Materials
MM 2.4: Vortrag
Montag, 5. September 2022, 11:00–11:15, H44
Tackling structural complexity in Li2S-P2S5 solid-state electrolytes using Machine Learning — •Tabea Huss, Carsten Staacke, Johannes Margraf, Karsten Reuter, and Christoph Scheurer — Fritz-Haber-Institut der MPG
The lithium thiophosphate (LPS) material class provides promising candidates for solid-state electrolytes (SSE) 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. Ab-initio studies probing lithium ion conductivity are constrained in system size and simulated time scales. This limits the transferability of computational results to industrial applicable LPS materials. Therefore, we present the development of a data efficient training protocol for the LPS material class using the Gaussian Approximation Potential (GAP). The GAP model can likewise describe crystal and glassy materials and different P-S connectivities PmSn. We apply the GAP model to probe lithium ion conductivity and discuss the influence of poly-anions on the latter. The sampling approach allows for a variety of extension and transferability to other SSE.