Dresden 2026 – wissenschaftliches Programm
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MM: Fachverband Metall- und Materialphysik
MM 12: Materials for the Storage and Conversion of Energy III / Functional Materials I
MM 12.5: Vortrag
Dienstag, 10. März 2026, 11:30–11:45, SCH/A216
Beyond Ion Dynamics: Efficient Charge Transport Simulations including Electrons at Battery Scales — Matteo Rinaldi, Karsten Reuter, and •Christian Carbogno — Fritz-Haber-Institut der MPG, Berlin
Small polarons, i.e., atomically localized excess charges, are fundamental for charge transport in energy materials. A quantitative modeling of their dynamics is challenging, though, since it typically requires not only long time and length scales, but also an account of electronic degrees of freedom at an ab initio level. In this work, we overcome this hurdle via a machine-learning interatomic potential that explicitly accounts for small polarons, which are incorporated as semi-classical degrees of freedom that adiabatically follow the nuclei. Using HSE06 calculations [1] as training data, we validate the approach for BiVO4 and further demonstrate its power for lithium titanium oxide (LTO), a prototypical anode material featuring polarons [2]. By running accelerated sampling and large-scale molecular dynamics simulations, we clarify the mechanisms that drive ionic and polaronic transport as well as their coupling in LTO. This reveals that polarons do not merely serve as spectators, but thermodynamically adapt to the much slower ionic motion. This results in an increase of ionic conductivity, in line with experimental measurements. For the first time, this theoretically corroborates the occurrence of a correlated polaron-ion dynamics with profound implications for the design of energy materials.
[1] S. Kokott et al., J. Chem. Phys. 161, 024112 (2024).
[2] M. Kick et al., ACS Appl. Energy Mater. 4, 8583 (2021).
Keywords: Polarons; Battery Materials; LTO; Machine-Learning Interatomic Potentials