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. Furthermore, 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:
-
- Univ. of Connecticut, Storrs, CT (United States)
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1248893
- Report Number(s):
- LA-UR-15-26906
Journal ID: ISSN 2045-2322; srep20952
- Grant/Contract Number:
- AC52-06NA25396
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Scientific Reports
- Additional Journal Information:
- Journal Volume: 6; Journal ID: ISSN 2045-2322
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; computational methods; electronic devices
Citation Formats
Mannodi-Kanakkithodi, Arun, Pilania, Ghanshyam, Huan, Tran Doan, Lookman, Turab, and Ramprasad, Rampi. Machine learning strategy for accelerated design of polymer dielectrics. United States: N. p., 2016.
Web. doi:10.1038/srep20952.
Mannodi-Kanakkithodi, Arun, Pilania, Ghanshyam, Huan, Tran Doan, Lookman, Turab, & Ramprasad, Rampi. Machine learning strategy for accelerated design of polymer dielectrics. United States. https://doi.org/10.1038/srep20952
Mannodi-Kanakkithodi, Arun, Pilania, Ghanshyam, Huan, Tran Doan, Lookman, Turab, and Ramprasad, Rampi. Mon .
"Machine learning strategy for accelerated design of polymer dielectrics". United States. https://doi.org/10.1038/srep20952. https://www.osti.gov/servlets/purl/1248893.
@article{osti_1248893,
title = {Machine learning strategy for accelerated design of polymer dielectrics},
author = {Mannodi-Kanakkithodi, Arun and Pilania, Ghanshyam and Huan, Tran Doan and Lookman, Turab and Ramprasad, Rampi},
abstractNote = {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. Furthermore, 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.},
doi = {10.1038/srep20952},
journal = {Scientific Reports},
number = ,
volume = 6,
place = {United States},
year = {Mon Feb 15 00:00:00 EST 2016},
month = {Mon Feb 15 00:00:00 EST 2016}
}
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- arXiv