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

Crystal structure prediction of materials with high symmetry using differential evolution