title: 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 blocks in an evolutionary manner, thus directly leading to the design of polymers with given target properties. While this philosophy of learning to make instant predictions and design is demonstrated here for the example of polymer dielectrics, it is equally applicable to other classes of materials as well.
authors.affiliation: Department of Materials Science and Engineering, Institute of Materials Science, University of Connecticut, 97 North Eagleville Road, Storrs, Connecticut 06269, USA.
authors.forename: Arun
authors.initials: A
authors.lastname: Mannodi-Kanakkithodi
authors.affiliation: Materials Science and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA.
authors.forename: Ghanshyam
authors.initials: G
authors.lastname: Pilania
authors.affiliation: Department of Materials Science and Engineering, Institute of Materials Science, University of Connecticut, 97 North Eagleville Road, Storrs, Connecticut 06269, USA.
authors.forename: Tran Doan
authors.initials: TD
authors.lastname: Huan
authors.affiliation: Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA.
authors.forename: Turab
authors.initials: T
authors.lastname: Lookman
authors.affiliation: Department of Materials Science and Engineering, Institute of Materials Science, University of Connecticut, 97 North Eagleville Road, Storrs, Connecticut 06269, USA.
authors.forename: Rampi
authors.initials: R
authors.lastname: Ramprasad