FFCASP: A Massively Parallel Crystal Structure Prediction Algorithm

A new algorithm called Fast and Flexible CrystAl Structure Predictor (FFCASP) was developed to predict the structure of covalent and molecular crystals. FFCASP is massively parallel and able to handle more than 200 atoms in the unit cell (in other terms, it allows global optimization around 100 individual parameters). It uses a global optimizer specialized for Crystal Structure Prediction (CSP) which combines particle swarm and simulated annealing optimizers. Three different molecular crystals, including diverse intermolecular interactions, namely, cytosine, coumarin, and pyrazinamide, have been selected to evaluate the performance of FFCASP. While cytosine polymorphs have been searched by employing two different force fields (a DFT-SAPT based intermolecular potential and generalized amber force field (GAFF)) up to Z = 16, only GAFF has been used both in coumarin and pyrazinamide polymorph searches up to Z = 4. For these three molecular crystals, FFCASP generated more than 20 000 crystal structures, and the unique ones have been further treated by DFT-D3. A combination of data mining and a machine learning approach was introduced to determine the unique structures and their distribution into different clusters, which ultimately gives an opportunity to retrieve the common features and relations between the resulting structures. There are two known experimental crystal structures of cytosine, and both were successfully located with FFCASP. Two of the reported crystal structures of coumarin have been reproduced. Similarly, in pyrazinamide, three known experimental structures have been rediscovered. In addition to finding the experimentally known structures, FFCASP also located other low-energy structures for each considered molecular crystals. These successes of FFCASP offer the possibility to discover the polymorphic nature of other important molecular crystals (e.g., drugs) as well.本研究开发了一款名为快速灵活晶体结构预测器（Fast and Flexible CrystAl Structure Predictor, FFCASP）的新型算法，用于预测共价晶体与分子晶体的结构。FFCASP支持大规模并行计算，可处理晶胞内原子数超过200的体系（换言之，其可对约100个独立参数开展全局优化）。该算法采用专为晶体结构预测（Crystal Structure Prediction, CSP）设计的全局优化框架，结合了粒子群优化与模拟退火两种优化策略。 研究选取胞嘧啶、香豆素与吡嗪酰胺三种具有多样分子间相互作用的分子晶体，用于评估FFCASP的性能。针对胞嘧啶晶型的搜索，研究采用了两种不同的力场——基于对称性适配微扰理论的密度泛函（Density Functional Theory-Symmetry Adapted Perturbation Theory, DFT-SAPT）分子间势能场与通用Amber力场（generalized amber force field, GAFF），搜索范围最高至晶胞数目Z=16；而针对香豆素与吡嗪酰胺的晶型搜索，仅使用了GAFF力场，搜索范围最高至Z=4。 针对上述三种分子晶体，FFCASP共生成超过20000个晶体结构，其中的唯一结构经DFT-D3方法进一步优化处理。研究引入数据挖掘与机器学习相结合的方法，用于甄别唯一结构并将其归类至不同聚类簇中，最终得以提取所得结构间的共性特征与内在关联。 胞嘧啶已知存在两种实验晶体结构，二者均被FFCASP成功定位。香豆素的两种已报道晶体结构均被成功重现。同样，吡嗪酰胺的三种已知实验晶体结构也被成功复现。除了找到实验已知的晶体结构外，FFCASP还为每种研究的分子晶体找到了其他低能稳定结构。 FFCASP的上述成功应用案例，也为探索其他重要分子晶体（例如药物分子）的多晶型性质提供了可行途径。