Simulations with machine learning potentials identify the ion conduction mechanism mediating non-Arrhenius behavior in LGPS
Gavin Winter, Rafael G\'omez-Bombarelli

TL;DR
This study uses machine learning-enhanced molecular dynamics to explore ion conduction mechanisms in LGPS across a wide temperature range, revealing a phase transition near 400 K that affects ion correlation and conduction behavior.
Contribution
The paper introduces a neural network potential trained on DFT data enabling long-timescale MD simulations to analyze ion conduction in LGPS at near-DFT accuracy over a broad temperature range.
Findings
Confirmed a phase transition in LGPS around 400 K.
Observed reduced cation-cation correlation below 400 K.
Identified a shift from concerted to less correlated ion hopping near room temperature.
Abstract
LiGe(PS) (LGPS) is a highly concentrated solid electrolyte, in which Coulombic repulsion between neighboring cations is hypothesized as the underlying reason for concerted ion hopping, a mechanism common among superionic conductors such as LiLaZrO (LLZO) and LiAlTi(PO) (LATP). While first principles simulations using molecular dynamics (MD) provide insight into the Li transport mechanism, historically, there has been a gap in the temperature ranges studied in simulations and experiments. Here, we used a neural network (NN) potential trained on density functional theory (DFT) simulations, to run up to 40-nanosecond long MD simulations at DFT-like accuracy to characterize the ion conduction mechanisms across a range of temperatures that includes previous simulations and experimental studies. We have confirmed a Li…
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Taxonomy
TopicsAdvanced Battery Materials and Technologies · Fuel Cells and Related Materials · Conducting polymers and applications
