Critical assessment of regression-based machine learning methods for polymer dielectrics

Abstract

The design of new and improved materials for different applications of interest is boosted by combining computations or experiments with machine learning techniques. Materials scientists seek to use learning algorithms that can easily and efficiently be applied to their data in order to obtain quantitative property prediction models. Here, we utilize a first principles generated dataset of the electronic and dielectric properties of a chemical space of polymers to test different kinds of regression algorithms used by the machine learning community today. We explore several possibilities for the hyper-parameters that go into such learning schemes, and establish optimal strategies and parameters for high-fidelity polymer dielectrics property prediction models.