Temperature- and vacancy-concentration-dependence of heat transport in Li${}_3$ClO from multi-method numerical simulations

Abstract

Despite governing heat management in any realistic device, the microscopic mechanisms of heat transport in all-solid-state electrolytes are poorly known: existing calculations, all based on simplistic semi-empirical models, are unreliable for superionic conductors and largely overestimate their thermal conductivity. In this work, we deploy a combination of state-of-the-art methods to calculate the thermal conductivity of a prototypical Li-ion conductor, the Li${}_3$ClO antiperovskite. By leveraging ab initio, machine-learning, and force-field descriptions of inter-atomic forces, we are able to reveal the massive role of anharmonic interactions and diffusive defects on the thermal conductivity and its temperature dependence, and to eventually embed their effects into a simple rationale which is likely applicable to a wide class of ionic conductors.

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• Hydrodynamic finite-size scaling of the thermal conductivity in glasses
• Robustness of Local Predictions in Atomistic Machine Learning Models
• SporTran: a code to estimate transport coefficients from the cepstral analysis of (multivariate) current time series
• Thermal conductivity of Li${}_3$PS${}_4$ solid electrolytes with ab initio accuracy
• Predicting hot-electron free energies from ground-state data
• Prediction rigidities for data-driven chemistry
• Investigating finite-size effects in molecular dynamics simulations of ion diffusion, heat transport, and thermal motion in superionic materials
• Topology, oxidation states, and charge transport in ionic conductors