  The large amount of powder diffraction data for which the corresponding
crystal structures have not yet been identified suggests the existence of
numerous undiscovered, physically relevant crystal structure prototypes. In
this paper, we present a scheme to resolve powder diffraction data into crystal
structures with precise atomic coordinates by screening the space of all
possible atomic arrangements, i.e., structural prototypes, including those not
previously observed, using a pre-trained machine learning (ML) model. This
involves (i) enumerating all possible symmetry-confined ways in which a given
composition can be accommodated in a given spacegroup, (ii) ranking the
element-assigned prototype representations using energies predicted using the
Wren ML model [Sci.Adv.8, eabn4117 (2022)], (iii) assigning and perturbing
atoms along the degree of freedom allowed by the Wyckoff positions to match the
experimental diffraction data (iv) validating the thermodynamic stability of
the material using density-functional theory (DFT). An advantage of the
presented method is that it does not rely on a database of previously observed
prototypes and is, therefore capable of finding crystal structures with
entirely new symmetric arrangements of atoms. We demonstrate the workflow on
unidentified XRD spectra from the ICDD database and identify a number of stable
structures, where a majority turns out to be derivable from known prototypes.
However, at least two are found not to be part of our prior structural data
sets.
