  Crystal structure prediction (CSP) has made significant progress, but most
methods focus on unconditional generations of inorganic crystal with limited
atoms in the unit cell. This study introduces XtalNet, the first equivariant
deep generative model for end-to-end CSP from Powder X-ray Diffraction (PXRD).
Unlike previous methods that rely solely on composition, XtalNet leverages PXRD
as an additional condition, eliminating ambiguity and enabling the generation
of complex organic structures with up to 400 atoms in the unit cell. XtalNet
comprises two modules: a Contrastive PXRD-Crystal Pretraining (CPCP) module
that aligns PXRD space with crystal structure space, and a Conditional Crystal
Structure Generation (CCSG) module that generates candidate crystal structures
conditioned on PXRD patterns. Evaluation on two MOF datasets (hMOF-100 and
hMOF-400) demonstrates XtalNet's effectiveness. XtalNet achieves a top-10 Match
Rate of 90.2% and 79% for hMOF-100 and hMOF-400 datasets in conditional crystal
structure prediction task, respectively. XtalNet represents a significant
advance in CSP, enabling the prediction of complex structures from PXRD data
without the need for external databases or manual intervention. It has the
potential to revolutionize PXRD analysis. It enables the direct prediction of
crystal structures from experimental measurements, eliminating the need for
manual intervention and external databases. This opens up new possibilities for
automated crystal structure determination and the accelerated discovery of
novel materials.
