Estimation of Dielectric Constant of Polymer Melt by Machine Learning Considering with Higher-order Structure

The polymer MI (Materials Informatics) method for exploring the properties of polymer melts has not yet been established because no descriptor that can describe higher-order structures has been found. From past studies, we have confirmed that the higher-order structure of polymer melt can be expressed by interpreting the coordinate data of all-atom MD using persistent homology, which is one of TDA (Topological Data Analysis). In this study, a learning model has been constructed by machine learning using a persistent diagram vectorized from a comprehensive data set of various types of the polymer melt as a descriptor and a dielectric constant as the objective variable. As a result, it has been shown that a model with high prediction accuracy based on a descriptor with physical meaning was constructed, a search space was defined by using SOM, and an unknown region could be estimated by Gaussian process regression.