Date of Completion

1-12-2015

Embargo Period

1-5-2015

Keywords

polymer dielectrics, machine learning, first principles

Major Advisor

Ramamurthy (Rampi) Ramprasad

Associate Advisor

George Rossetti, Jr.

Associate Advisor

Avinash M. Dongare

Field of Study

Materials Science and Engineering

Degree

Doctor of Philosophy

Open Access

Open Access

Abstract

Polymers offer a nearly infinite variety of material systems with diverse properties.

Until recently, the formulation of polymers for specific applications was based

on trial and error, guided by intuition. In this work, first principles computations

and machine learning approach are employed to guide the design of polymers, in

the present case for dielectric applications. Specically, we adopt two strategies, (1)

functionalization of a well understood polymer dielectrics, such as PE and PP, to

enhance its dielectric response, and (2) discovery of entirely new classes of polymer

dielectrics, both organic and organometallic. Different polymer classes are explored,

from C-based organic polymers to novel Si-, Ge-, and Sn-based polymers, and the

search is based on two properties, band gap and dielectric constant. Newly developed

high throughput DFT methods were used first to accurately determine the

dielectric constant and band gap of dierent polymer systems for a set of limited

compositions and congurations. Machine learning methods were then used to predict

the properties of systems spanning a much larger part of the congurational

and compositional space. Based on this strategy, we are able to provide a "map" of

the achievable combination of properties within the chemical space explored.

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