论文标题
监督和无监督的机器学习聚合物的结构阶段吸附到纳米线上
Supervised and Unsupervised Machine Learning of Structural Phases of Polymers Adsorbed to Nanowires
论文作者
论文摘要
我们通过机器学习确定聚合物纳米管复合材料中的构型阶段和结构过渡。我们采用各种无监督的降维方法，常规的神经网络以及混乱方法，这是一种基于神经网络的方法。我们发现神经网络能够可靠地识别所有在实验和仿真中发现的配置阶段。此外，我们以消除人类直觉或偏见的方式定位配置阶段之间的边界。这只能在依靠先入为主的临时顺序参数之前完成。
We identify configurational phases and structural transitions in a polymer nanotube composite by means of machine learning. We employ various unsupervised dimensionality reduction methods, conventional neural networks, as well as the confusion method, an unsupervised neural-network-based approach. We find neural networks are able to reliably recognize all configurational phases that have been found previously in experiment and simulation. Furthermore, we locate the boundaries between configurational phases in a way that removes human intuition or bias. This could be done before only by relying on preconceived, ad-hoc order parameters.
