标题： 聚合物晶体成核模式的机器学习模型分析 显示英文标题

标题： Nucleation patterns of polymer crystals analyzed by machine learning models

摘要： 我们使用机器学习算法来检测过冷熔体中的晶体相，在分子动力学模拟中。 我们的分类方法基于单体的局部构象和环境指纹。 特别是，我们采用自监督自动编码器来压缩指纹信息，并使用高斯混合模型来区分有序状态和无序状态。 结果表明，晶体单体的识别与人类定义的分类器在很大程度上一致，例如我们在之前的工作中开发的基于茎长的分类方案 [C. Luo 和 J.-U. Sommer, Macromolecules 44 (2011), 1523]，但不需要任何关于半晶态聚合物结构的先验知识。 由于其局部敏感性，该方法能够解析晶体有序的详细时间模式，而在热力学性质（如比体积）中出现过渡的明显特征之前。 在预过渡点，我们观察到使用晶体相中保留的单体分数与加入该相的单体数量相比，结晶效率最高。 显示英文摘要

摘要： We use machine learning algorithms to detect the crystalline phase in undercooled melts in molecular dynamics simulations. Our classification method is based on local conformation and environmental fingerprints of individual monomers. In particular, we employ self-supervised auto-encoders to compress the fingerprint information and a Gaussian mixture model to distinguish ordered states from disordered ones. The resulting identification of crystalline monomers agrees to a large extent with human-defined classifiers such as the stem-length-based classification scheme as developed in our previous work [C. Luo and J.-U. Sommer, Macromolecules 44 (2011), 1523], but does not require any foreknowledge about the structure of semi-crystalline polymers. Because of its local sensitivity, the method allows the resolution of detailed time patterns of crystalline order before an apparent signature of the transition is visible in thermodynamic properties such as for the specific volume. At a pre-transition point, we observe the highest crystallization efficiency using the fraction of monomers being conserved in the crystalline phase as compared to the number of monomers joining that phase.