Machine Learning Strategy for Accelerated Design of Polymer Dielectrics.

ABSTRACT: The ability to efficiently design new and advanced dielectric polymers is hampered by the lack of sufficient, reliable data on wide polymer chemical spaces, and the difficulty of generating such data given time and computational/experimental constraints. Here, we address the issue of accelerating polymer dielectrics design by extracting learning models from data generated by accurate state-of-the-art first principles computations for polymers occupying an important part of the chemical subspace. The polymers are 'fingerprinted' as simple, easily attainable numerical representations, which are mapped to the properties of interest using a machine learning algorithm to develop an on-demand property prediction model. Further, a genetic algorithm is utilised to optimise polymer constituent bloc

SUBMITTER: Mannodi-Kanakkithodi A 

PROVIDER: S-EPMC4753456 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature