Yang Wenhui, Dilanga Siriwardane Edirisuriya M, Dong Rongzhi, Li Yuxin, Hu Jianjun
School of Mechanical Engineering, Guizhou University, Guiyang 550025, People's Republic of China.
Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29201, United States of America.
J Phys Condens Matter. 2021 Aug 31;33(45). doi: 10.1088/1361-648X/ac1d6c.
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), demonstrating that known geometric constraints (such as the contact map of the crystal structure) help the crystal structure reconstruction. However, when predicting the crystal structure with high symmetry, we found that the global optimization algorithm has difficulty to find an effective combination of WP 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 WP 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.
晶体结构决定材料的性质。借助化学物质的晶体结构，许多物理和化学性质可通过第一性原理计算或机器学习模型进行预测。由于生成一个假设的化学有效公式相对容易，晶体结构预测成为发现新材料的重要方法。在我们之前的工作中，我们提出了一种基于接触图的晶体结构预测方法，该方法使用遗传算法等全局优化算法，使预测结构的接触图与真实晶体结构的接触图之间的匹配最大化，以搜索魏科夫位置（WP）处的坐标，证明了已知的几何约束（如晶体结构的接触图）有助于晶体结构重建。然而，在预测具有高对称性的晶体结构时，我们发现全局优化算法难以找到满足化学式的WP的有效组合方式，这主要是由预测晶体结构的接触图维度与目标晶体结构的接触图维度不一致所致。这使得预测高对称晶体的结构具有挑战性。为了解决这个问题，在此我们提议使用PyXtal基于化学式和空间群等信息，生成并筛选具有给定对称约束的随机晶体结构。以接触图作为优化目标，我们使用差分进化算法在WP处搜索非特殊坐标，以实现高对称晶体材料的结构预测。我们的实验结果表明，我们提出的算法CMCrystalHS能够有效解决接触图维度不一致的问题，并预测具有高对称性的晶体结构。
