Genarris：分子晶体结构的随机生成和Harris近似的快速筛选
Genarris: Random Generation of Molecular Crystal Structures and Fast Screening with a Harris Approximation
摘要：
我们提出了Genarris，一个Python软件包，通过随机采样与物理约束，对刚性分子的分子晶体进行配置空间筛选。对于快速能量评估，Genarris采用Harris近似，其中分子晶体的总密度通过单分子密度的叠加来构建。色散包容的密度泛函理论（DFT），然后用于哈里斯密度，而不执行自洽循环。Genarris使用机器学习进行聚类，基于专门为分子晶体开发的相对坐标描述符（RCD），该描述符在识别包装基序相似性方面具有鲁棒性。除了随机结构生成之外，Genarris还提供了三种基于不同顺序的连续聚类和选择步骤的工作流程：“严格”工作流程是对潜在能量景观的详尽探索，“能量”工作流程产生一组低能量结构，“多样化”工作流程产生最大限度多样化的结构。后者被推荐用于生成遗传算法的初始种群。在这里，Genarris的实施报告和它的应用程序演示了三个测试用例。
英文摘要：
We present Genarris, a Python package that performs configuration space screening for molecular crystals of rigid molecules by random sampling with physical constraints. For fast energy evaluations Genarris employs a Harris approximation, whereby the total density of a molecular crystal is constructed via superposition of single molecule densities. Dispersion-inclusive density functional theory (DFT) is then used for the Harris density without performing a self-consistency cycle. Genarris uses machine learning for clustering, based on a relative coordinate descriptor (RCD) developed specifically for molecular crystals, which is shown to be robust in identifying packing motif similarity. In addition to random structure generation, Genarris offers three workflows based on different sequences of successive clustering and selection steps: the "Rigorous" workflow is an exhaustive exploration of the potential energy landscape, the "Energy" workflow produces a set of low energy structures, and the "Diverse" workflow produces a maximally diverse set of structures. The latter is recommended for generating initial populations for genetic algorithms. Here, the implementation of Genarris is reported and its application is demonstrated for three test cases.
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