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

To date, experimental and theoretical works have been unable to uncover the ground-state configuration of the solid electrolyte cubic Li7La3Zr2O12 (c-LLZO). Computational studies rely on an initial low-energy structure as a reference point. Here, we present a methodology for identifying energetically favorable configurations of c-LLZO for a crystallographically predicted structure. We begin by eliminating structures that involve overlapping Li atoms based on nearest neighbor counts. We further reduce the configuration space by eliminating symmetry images from all remaining structures. Then, we perform 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. We provide a database containing all of the generated structures with the aim of improving accuracy and reproducibility in future c-LLZO research.