论文标题
数据驱动的识别和分析聚合物融化中的玻璃转变
Data-driven identification and analysis of the glass transition in polymer melts
论文作者
论文摘要
在实验和理论聚合物科学中，了解聚合物材料的玻璃过渡温度的性质以及对聚合物材料的玻璃转变温度的精确估计。我们提出了一种数据驱动的方法，该方法利用可以通过分子动力学模拟访问的高分辨率细节，并考虑了单个链的结构信息。它清楚地识别了弱半串联链的聚合物熔体的玻璃过渡温度。通过将主成分分析和聚类结合起来，我们即使从相对短时的轨迹中识别出渐近极限中的玻璃过渡温度，这些温度刚刚进入了类似唤醒的单体位移状态。我们证明，主成分分析捕获的波动反映了链的行为的变化：从上方的构象重排到玻璃过渡温度以下的小重排。我们的方法很容易应用，应适用于其他聚合物玻璃制成液体。
Understanding the nature of glass transition, as well as precise estimation of the glass transition temperature for polymeric materials, remain open questions in both experimental and theoretical polymer sciences. We propose a data-driven approach, which utilizes the high-resolution details accessible through the molecular dynamics simulation and considers the structural information of individual chains. It clearly identifies the glass transition temperature of polymer melts of weakly semiflexible chains. By combining principal component analysis and clustering, we identify the glass transition temperature in the asymptotic limit even from relatively short-time trajectories, which just reach into the Rouse-like monomer displacement regime. We demonstrate that fluctuations captured by the principal component analysis reflect the change in a chain's behaviour: from conformational rearrangement above to small rearrangements below the glass transition temperature. Our approach is straightforward to apply, and should be applicable to other polymeric glass-forming liquids.
