  Crystal structure determines properties of materials. With the crystal
structure of a chemical substance, many physical and chemical properties can be
predicted by first-principles calculations or machine learning models. Since it
is relatively easy to generate a hypothetical chemically valid formula, crystal
structure prediction becomes an important method for discovering new materials.
In our previous work, we proposed a contact map-based crystal structure
prediction method, which uses global optimization algorithms such as genetic
algorithms to maximize the match between the contact map of the predicted
structure and the contact map of the real crystal structure to search for the
coordinates at the Wyckoff Positions(WP). However, when predicting the crystal
structure with high symmetry, we found that the global optimization algorithm
has difficulty to find an effective combination of WPs that satisfies the
chemical formula, which is mainly caused by the inconsistency between the
dimensionality of the contact map of the predicted crystal structure and the
dimensionality of the contact map of the target crystal structure. This makes
it challenging to predict the crystal structures of high-symmetry crystals. In
order to solve this problem, here we propose to use PyXtal to generate and
filter random crystal structures with given symmetry constraints based on the
information such as chemical formulas and space groups. With contact map as the
optimization goal, we use differential evolution algorithms to search for
non-special coordinates at the Wyckoff positions to realize the structure
prediction of high-symmetry crystal materials. Our experimental results show
that our proposed algorithm CMCrystalHS can effectively solve the problem of
inconsistent contact map dimensions and predict the crystal structures with
high symmetry.
