Berlin 2024 – scientific programme

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MM: Fachverband Metall- und Materialphysik

MM 21: Transport in Materials: Diffusion, Conduction of Charge or Heat I

MM 21.2: Talk

Tuesday, March 19, 2024, 10:30–10:45, C 264

Transport mechanism in Lithium thiophosphate — •Davide Tisi, Lorenzo Gigli, Federico Grasselli, and Michele Ceriotti — Ecole Polytechnique Federale de Lausanne (EPFL)

Lithium ortho-thiophosphate (Li3PS4) are a promising candidate for solid-state-electrolyte batteries. The microscopic mechanisms of Li-ion transport in Li3PS4 are, still, far from being fully understood, and no computational work has tackled the thermal conductivity at DFT level.

In this talk, I will show how we build multi-level machine learning potentials targeting state-of-the-art DFT references (PBEsol, SCAN, and PBE0), to study the electrical and thermal conductivity of all the known phases of Li3PS4 (α, β and γ). I will discuss the physical origin of the superionic behaviour of Li3PS4: the activation of PS4 flipping drives a structural phase transition to a highly conductive phase, characterised by an enhancement of Li-site availability and by a drastic reduction in the activation energy of Li-ion diffusion. I will show the effects of the phase transition on both the electrical and thermal conductivity. We elucidate the role of inter-ionic dynamical correlations in charge transport, by highlighting the failure of the Nernst-Einstein approximation to estimate the electrical conductivity. Finally, we compare the thermal conductivity computed by the Green-Kubo theory with the results from the Boltzmann transport equation, to highlight the role of anharmonicity and quantum effects.

Our results show a dependence on the target DFT reference, with PBE0 yielding the best quantitative agreement with experiments.

Keywords: machine learning potentials; Li-ion solid state electrolytes; phase transition; Green-Kubo; hybrid funcional