polyG2G: A Novel Machine Learning Algorithm Applied to the Generative Design of Polymer Dielectrics-Reference-Cited by-同舟云学术

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polyG2G: A Novel Machine Learning Algorithm Applied to the Generative Design of Polymer Dielectrics

Published:2021-08-31 Issue:17 Volume:33 Page:7008-7016
ISSN:0897-4756
Container-title:Chemistry of Materials
language:en
Short-container-title:Chem. Mater.

Author:

Gurnani Rishi¹^ORCID,Kamal Deepak¹^ORCID,Tran Huan¹^ORCID,Sahu Harikrishna¹^ORCID,Scharm Kenny²,Ashraf Usman³,Ramprasad Rampi¹^ORCID

Affiliation:

1. School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States

2. College of Computing, Georgia Institute of Technology, Atlanta, Georgia 30332, United States

3. School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States

Funder

Basic Energy Sciences

Office of Naval Research

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.chemmater.1c02061

Reference45 articles.

1. Advanced polymeric dielectrics for high energy density applications

2. Rational design of all organic polymer dielectrics

3. Rational Co-Design of Polymer Dielectrics for Energy Storage

4. Computable Bulk and Interfacial Electronic Structure Features as Proxies for Dielectric Breakdown of Polymers

5. Emerging materials intelligence ecosystems propelled by machine learning

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