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Temperature- and vacancy-concentration-dependence of heat transport in Li₃ClO from multi-method numerical simulations

Paolo Pegolo1*, Stefano Baroni1,2*, Federico Grasselli3,1*

1 SISSA—Scuola Internazionale Superiore di Studi Avanzati, 34136 Trieste, Italy

2 CNR—Istituto Officina dei Materiali, SISSA, 34136 Trieste, Italy

3 COSMO—Laboratory of Computational Science and Modelling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland

* Corresponding authors emails: ppegolo@sissa.it, baroni@sissa.it, federico.grasselli@epfl.ch
DOI10.24435/materialscloud:hf-qj [version v1]

Publication date: Jan 11, 2022

How to cite this record

Paolo Pegolo, Stefano Baroni, Federico Grasselli, Temperature- and vacancy-concentration-dependence of heat transport in Li₃ClO from multi-method numerical simulations, Materials Cloud Archive 2022.3 (2022), doi: 10.24435/materialscloud:hf-qj.


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 Li₃ClO antiperovskite. By leveraging ab initio, machine learning, and forcefield descriptions of interatomic 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. In this record, we provide data and scripts to generate the plots supporting our findings. We also provide the machine learning model and the dataset to train it.

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File name Size Description
3.3 KiB Information on the files and instructions.
1.7 MiB Archive with data and scripts to generate the main plots in the paper.
42.7 MiB Archive with the machine-learning model and the dataset used to train it.


Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Preprint (Preprint where the data is discussed.)


heat transport superionics batteries solid-state-electrolytes MaX SNSF

Version history:

2022.3 (version v1) [This version] Jan 11, 2022 DOI10.24435/materialscloud:hf-qj