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Accelerated workflow for antiperovskite-based solid state electrolytes

Benjamin H. Sjølin1*, Peter B. Jørgensen1*, Andrea Fedrigucci1,2*, Tejs Vegge1*, Arghya Bhowmik1*, Ivano E. Castelli1*

1 Department of Energy Conversion and Storage, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark

2 Theory and Simulation of Materials, École Polytechnique Fédérale de Lausanne (EPFL), Station 9, CH-1015 Lausanne, Switzerland

* Corresponding authors emails: benjas@dtu.dk, pbjo@dtu.dk, Andrea.fedrigucci@epfl.ch, teve@dtu.dk, arbh@dtu.dk, ivca@dtu.dk
DOI10.24435/materialscloud:39-xs [version v1]

Publication date: May 23, 2023

How to cite this record

Benjamin H. Sjølin, Peter B. Jørgensen, Andrea Fedrigucci, Tejs Vegge, Arghya Bhowmik, Ivano E. Castelli, Accelerated workflow for antiperovskite-based solid state electrolytes, Materials Cloud Archive 2023.80 (2023), doi: 10.24435/materialscloud:39-xs.


We developed and implemented a multi-target multi-fidelity workflow to explore the chemical space of antiperovskite materials with general formula X3BA (X = Li, Na, Mg) and PM-3m space group, searching for stable high-performance solid state electrolytes for all-solid state batteries. The workflow is based on the calculation of thermodynamic and kinetic properties, which include phase and electrochemical stability, semiconducting behaviour, and ionic diffusivity. To accelerate the calculation of the kinetic properties, we use a surrogate model able to predict the transition state structure during ionic diffusion. This reduces the calculation cost by more than one order of magnitude while keeping the mean error within 73 meV compared to the more accurate nudged elastic band method. This method allows us identify 14 materials that agree with the experimentally reported results as some of the best solid state electrolytes. Moreover, this approach is general and chemistry neutral, so can be applied to other battery chemistries and crystal prototypes. We report here the data produced in this work. These results are collected as ASE database. It contains a markdown file that explains the usage and most important data.

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File name Size Description
41.5 MiB Database
3.1 KiB Database usage


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Solid-state Electrolytes Antiperovskites Autonomous Workflow Surrogate Models High-throughput Screening NEB Calculations

Version history:

2023.80 (version v1) [This version] May 23, 2023 DOI10.24435/materialscloud:39-xs