  We developed a density functional theory-free approach for crystal structure
prediction via combing graph network (GN) and Bayesian optimization (BO). GN is
adopted to establish the correlation model between crystal structure and
formation enthalpies. BO is to accelerate searching crystal structure with
optimal formation enthalpy. The approach of combining GN and BO for crystal
Structure Searching (GN-BOSS), in principle, can predict crystal structure at
given chemical compositions without additional constraints on cell shapes and
lattice symmetries. The applicability and efficiency of GN-BOSS approach is
then verified via solving the classical Ph-vV challenge. It can correctly
predict the crystal structures of 24 binary compounds from scratch with
averaged computational cost ~ 30 minutes each by only one CPU core. GN-BOSS
approach may open a new avenue to data-driven crystal structural prediction
without using the expensive DFT calculations.
