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MM: Fachverband Metall- und Materialphysik

MM 6: Transport in Materials: Ion, Charge and Heat Transport

MM 6.3: Vortrag

Montag, 27. März 2023, 10:45–11:00, SCH A 118

Accelerating structure prediction of solid-solid interfaces in solid electrolytes using Machine Learning Potentials — •Tabea Huss, Carsten Staacke, 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 structural disorder and occur in a multitude of glassy and crystalline phases, depending on their stochiometry. The most performant glass-ceramic SSEs from the LPS class are characterized by omnipresent two-dimensional interfaces between crystalline and glassy domains, which can dominate the materials performance and cycle stability. To address this complexity we present a protocol for the construction of polycrystalline solid-solid interfaces in the LPS system. Within our protocol, expensive ab-initio random structure search (AIRSS) calculations are replaced by a Machine Learning surrogate accelerated approach. We present a pathway towards a full assessment of partially amorphous interfaces in the LPS material class.