Screening the unexplored crystal prototype space and inverting XRD patterns with the WREN machine-learning model

The WREN (Wyckoff REpresentation regressioN) machine-learning model trained on 300k formation energies across the full chemical space allows near-instant prediction of formation energies of materials just from their element-assigned crystal prototypes (expressed in terms of Wyckoff positions) [1]. This model allows screening for materials with desired properties among structures fundamentally different from those presently catalogued in materials databases. This talk presents the WREN model and demonstrates our recent progress in using it to invert XRD patterns. Our highly efficient implementation enumerates candidate prototypes, uses WREN to order them by formation energy, and then optimizes the remaining degrees of freedom to match the XRD peaks. The approach is shown capable of resolving previously unresolved XRD patterns in the ICDD database.

[1] https://doi.org/10.1126/sciadv.abn4117