Identifying Conformation States of Polymer through Unsupervised Machine Learning

The study of the critical behavior is important for classifying different configuration states. Recently, machine learning is capable of discriminating polymer states in the presence of human supervision. Here, we introduce an unsupervised approach based on the self-organizing map (SOM) and the autoencoder network to locate critical phase transitions from raw configuration without the necessity for manual feature engineering. High-dimensional configuration data can be encoded to low-dimensional codes by employing neural network of multilayer restrictive Boltzmann machines and the intermediate code can also be reconstructed to high-dimensional input vector. And then the codes are used to cluster different configuration states for polymers adsorbed on the homogeneous and the stripe-patterned surface by the SOM network and K-Means method. This work presents an unusual tool to identify polymer configuration.