Berlin 2024 – wissenschaftliches Programm
Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe
MM: Fachverband Metall- und Materialphysik
MM 37: Transport in Materials: Diffusion, Conduction of Charge or Heat IV
MM 37.1: Vortrag
Mittwoch, 20. März 2024, 11:45–12:00, C 264
Atomistic modelling of bulk and grain boundary diffusion for solid electrolyte Li6PS5Cl — •Yongliang Ou1, Yuji Ikeda1, Sergiy Divinski2, and Blazej Grabowski1 — 1Institute for Materials Science, University of Stuttgart, 70569 Stuttgart, Germany — 2Institute of Materials Physics, University of Münster, 48149 Münster, Germany
Li6PS5Cl is a promising candidate for the solid electrolyte in all-solid-state Li-ion batteries. In applications, this material exits in a polycrystalline state with many grain boundaries (GBs) rather than a single-crystalline state. Atomistic modeling of Li6PS5Cl with GBs, however, remains rare due to high computational cost. Here, machine-learning interatomic potentials, specifically moment tensor potentials (MTPs), are employed to accelerate simulations while preserving the ab initio accuracy. Two tilt GBs Σ3(112)[110], Σ3(111)[110] and one twist GB Σ5(001)[001] are investigated, all of which exhibit low GB energies based on the annealing and quenching relaxation approach. Diffusion coefficients are calculated through mean square displacements obtained by molecular dynamics simulations. Enhanced Li diffusion compared to the perfect bulk is observed at GBs. A connection between GB morphology and its effects on Li diffusion is revealed, paving the way for improved solid electrolyte design through GB engineering.
Keywords: molecular dynamics; machine learning interatomic potentials; Li diffusion; solid electrolytes; grain boundary