A Workflow for Identifying Viable Crystal Structures with Partially Occupied Sites Applied to Solid Electrolyte: Cubic Li7La3Zr2O12

03 August 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Experimental and theoretical works have, to date, been unable to uncover the ground state configuration of prominent solid electrolyte candidate cubic Li$_7$La$_3$Zr$_2$O$_{12}$ (c-LLZO). Computational studies rely on an initial low-energy structure as a reference point. In this study, we present a methodology to identify energetically favourable configurations of c-LLZO, enabling the isolation of low-energy structures, for a crystallographically predicted structure. We begin by eliminating structures that involve overlapping Li atoms based on nearest neighbour counts. We further reduce the configuration space by eliminating symmetry images from all remaining structures. This is followed up with a machine learning-based energetic ordering of all remaining structures. By considering the geometrical constraints that emerge from this methodology we determine that a large portion of previously reported structures may not be feasible or stable. The method developed here could be extended to other ion conductors and partially occupied crystals. Furthermore, we provide all structures generated in a freely accessible database with the aim to improve accuracy and reproducibility in future c-LLZO research.


Machine Learning
Local Sensitivity Hashing
Structure Generation


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Comment number 1, Julian Holland: Aug 22, 2023, 14:02

The data set will be released with the DOI given in the paper when the paper is published. However in the mean-time you can access the database with all structures and accrued data here: https://eprints.soton.ac.uk/480550/