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MM: Fachverband Metall- und Materialphysik
MM 29: Materials for Energy Storage and Conversion - Battery and Fuel Cell Materials (joint session MM/CPP)
MM 29.4: Vortrag
Dienstag, 17. März 2020, 15:00–15:15, IFW D
A Neural Network Potential for Lithium Manganese Oxides — •Marco Eckhoff1, Peter E. Blöchl2, and Jörg Behler1 — 1Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie, Tammannstraße 6, 37077 Göttingen, Germany — 2Technische Universität Clausthal, Institut für Theoretische Physik, Leibnizstraße 10, 38678 Clausthal-Zellerfeld, Germany
The lithium manganese oxide spinel LixMn2O4, with 0<x<2, is an important cathode material in lithium ion batteries. The recently introduced local hybrid density functional PBE0r yields an accurate description of this material in good agreement with experiment. However, the accessible system size of molecular dynamics and Monte Carlo simulations is very limited when using density functional theory directly. Building on PBE0r data, we thus constructed a high-dimensional neural network potential, which provides a first-principles quality description of the potential energy surface at a fraction of the computational costs. This potential enables large-scale simulations of LixMn2O4 to study phase transitions and lithium diffusion.