  The prediction of material structure from chemical composition has been a
long-standing challenge in natural science. Although there have been various
methodological developments and successes with computer simulations, the
prediction of crystal structures comprising more than several tens of atoms in
the unit cell still remains difficult due to the many degrees of freedom, which
increase exponentially with the number of atoms. Here we show that when some
experimental data is available, even if it is totally insufficient for
conventional structure analysis, it can be utilized to support and
substantially accelerate structure simulation. In particular, we formulate a
cost function based on a weighted sum of interatomic potential energies and a
penalty function referred to as "crystallinity", which is defined using limited
X-ray diffraction data. This method is applied to well-known polymorphs of
$\rm{SiO_2}$ with up to 96 atoms in the simulation cell to find that it
reproduces the correct structures efficiently with a very limited number of
diffraction peaks. The penalty function is confirmed to destabilize the local
minima of the potential energy surface, which facilitates finding the correct
structure. This method opens a new avenue for determining and predicting
structures that are difficult to determine by conventional methods, such as
surface, interface, glass, and amorphous structures.
