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MM: Fachverband Metall- und Materialphysik
MM 56: Materials for Energy Storage and Conversion - Intercalation
MM 56.5: Vortrag
Donnerstag, 19. März 2020, 12:45–13:00, IFW D
The Electrostatic GAP: Machine-Learning Potentials for Battery Materials — •Carsten Staacke1, Christoph Scheurer1, Johannes Margraf1, Gabor Csanyi2, and Karsten Reuter1 — 1Chair of Theoretical Chemistry, TUM, Germany — 2Engineering Department, Cambridge University, UK
All-solid-state Li-ion batteries promise gains in safety and durability by combining high Li-ion conductivity and mechanical ductility. In this respect, solid-state electrolytes (SSE) such as the Li7P3S11 glass-ceramic have gained much attention.[1] From a modelling perspective, describing ionic conductivity and the role of crystalline/amorphous interfaces in such SSEs requires an accurate and efficient description of covalent and electrostatic interactions. To this end, we have combined short-ranged machine-learning potentials based on the Gaussian Approximation Potential (GAP)[2] approach with a classical electrostatic model in the long-range. We will present a first-principles validation of both, pure GAP potential and the new electrostatic GAP for the LPS SSE. In particular, the role of Coulomb interactions in SSE simulations will be shown and evaluated.
[1] A. Hayashi, A. Sakuda, M. Tatsumisago, Front. Energy Res., 2016, 4, 25
[2] A. P. Bartók, M. C. Payne, R. Kondor, G. Csányi Phys. Rev. Lett., 2010, 104, 136403