School of Materials Science and Engineering, Georgia Institute of Technology, 771 Ferst Drive NW, Atlanta, Georgia 30332, United States.
J Phys Chem Lett. 2020 Aug 6;11(15):5823-5829. doi: 10.1021/acs.jpclett.0c01553. Epub 2020 Jul 8.
Developing a large database of polymers structures and properties, for which suitable polymer structural models are a prerequisite, is critical for polymer informatics. We present a simple strategy, referred to as polymer structure predictor (PSP), for predicting the crystal structural models of linear polymers, given their chain-level atomic connectivity information. The PSP, which was designed specifically for polymers, relies on properly defining and sampling the configuration space. Using this approach, we have successfully recovered eight known crystal structures of six common linear polymers, including polyethylene, polyvinylidene fluoride, poly(vinyl chloride), poly(-phenylene sulfide), polyacrylonitrile, and poly-2,5-benzoxazole, while discovering some new stable structures of three of them, i.e., polyvinylidene fluoride, polyacrylonitrile, and poly(-phenylene sulfide). The PSP is very simple, highly scalable, suitable for automatic workflows, and comparable to the best major structure prediction method in terms of efficiency in polymer crystal structure prediction. Although challenges in comprehensively accounting for possible chain-level conformations remain, the PSP will be very useful in efficiently generating polymer data and strengthening the emerging polymer informatics ecosystems.
开发大型聚合物结构和性质数据库对于聚合物信息学至关重要，而这需要合适的聚合物结构模型作为前提。我们提出了一种简单的策略，称为聚合物结构预测器（PSP），用于根据线性聚合物的链级原子连接信息预测其晶体结构模型。PSP 是专门为聚合物设计的，依赖于正确定义和采样构象空间。使用这种方法，我们成功地恢复了六种常见线性聚合物（包括聚乙烯、聚偏二氟乙烯、聚氯乙烯、聚（苯硫醚）、聚丙烯腈和聚-2,5-苯并恶唑）的八个已知晶体结构，同时发现了其中三种聚合物（即聚偏二氟乙烯、聚丙烯腈和聚（苯硫醚））的一些新的稳定结构。PSP 非常简单，具有高度可扩展性，适用于自动化工作流程，在聚合物晶体结构预测方面的效率可与最佳主要结构预测方法相媲美。尽管在全面考虑可能的链级构象方面仍然存在挑战，但 PSP 将在高效生成聚合物数据和加强新兴的聚合物信息学生态系统方面非常有用。
