Multi-objective optimization techniques to design the Pareto front of organic dielectric polymers
Abstract
Here, we present two Monte Carlo algorithms to find the Pareto front of the chemical space of a class of dielectric polymers that is most interesting with respect to optimizing both the bandgap and dielectric constant. Starting with a dataset generated from density functional theory calculations, we used machine learning to construct surrogate models for the bandgaps and dielectric constants of all physically meaningful 4-block polymers (that is, polymer systems with a 4-block repeat unit). We parameterized these machine learning models in such a way that the surrogates built for the 4-block polymers were readily extendable to polymers beyond a 4-block repeat unit. By using translational invariance, chemical intuition, and domain knowledge, we were able to enumerate all possible 4, 6, and 8 block polymers and benchmark our Monte Carlo sampling of the chemical space against the exact enumeration of the surrogate predictions. We obtained exact agreement for the fronts of 4-block polymers and at least a 90% agreement for those of 6 and 8-block polymers. We present fronts for 10-block polymer that are not possible to obtain by direct enumeration. We note that our Monte Carlo methods also return polymers close to the predicted front and a measure ofmore »
- Authors:
-
[2];
[2];
[2]
- Univ. of Connecticut, Storrs, CT (United States). Dept. of Materials Science and Engineering and Inst. of Materials Science
- 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 Laboratory Directed Research and Development (LDRD) Program; National Science Foundation (NSF). Extreme Science and Engineering Discovery Environment (XSEDE)
- OSTI Identifier:
- 1459808
- Alternate Identifier(s):
- OSTI ID: 1402407
- Report Number(s):
- LA-UR-16-25544
Journal ID: ISSN 0927-0256; TRN: US1901803
- Grant/Contract Number:
- AC52-06NA25396
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Computational Materials Science
- Additional Journal Information:
- Journal Volume: 125; Journal Issue: C; Journal ID: ISSN 0927-0256
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; 97 MATHEMATICS AND COMPUTING; Materials informatics; Density functional theory; Multi-objective optimization
Citation Formats
Mannodi-Kanakkithodi, Arun, Pilania, Ghanshyam, Ramprasad, Rampi, Lookman, Turab, and Gubernatis, James E. Multi-objective optimization techniques to design the Pareto front of organic dielectric polymers. United States: N. p., 2016.
Web. doi:10.1016/j.commatsci.2016.08.018.
Mannodi-Kanakkithodi, Arun, Pilania, Ghanshyam, Ramprasad, Rampi, Lookman, Turab, & Gubernatis, James E. Multi-objective optimization techniques to design the Pareto front of organic dielectric polymers. United States. https://doi.org/10.1016/j.commatsci.2016.08.018
Mannodi-Kanakkithodi, Arun, Pilania, Ghanshyam, Ramprasad, Rampi, Lookman, Turab, and Gubernatis, James E. Sat .
"Multi-objective optimization techniques to design the Pareto front of organic dielectric polymers". United States. https://doi.org/10.1016/j.commatsci.2016.08.018. https://www.osti.gov/servlets/purl/1459808.
@article{osti_1459808,
title = {Multi-objective optimization techniques to design the Pareto front of organic dielectric polymers},
author = {Mannodi-Kanakkithodi, Arun and Pilania, Ghanshyam and Ramprasad, Rampi and Lookman, Turab and Gubernatis, James E.},
abstractNote = {Here, we present two Monte Carlo algorithms to find the Pareto front of the chemical space of a class of dielectric polymers that is most interesting with respect to optimizing both the bandgap and dielectric constant. Starting with a dataset generated from density functional theory calculations, we used machine learning to construct surrogate models for the bandgaps and dielectric constants of all physically meaningful 4-block polymers (that is, polymer systems with a 4-block repeat unit). We parameterized these machine learning models in such a way that the surrogates built for the 4-block polymers were readily extendable to polymers beyond a 4-block repeat unit. By using translational invariance, chemical intuition, and domain knowledge, we were able to enumerate all possible 4, 6, and 8 block polymers and benchmark our Monte Carlo sampling of the chemical space against the exact enumeration of the surrogate predictions. We obtained exact agreement for the fronts of 4-block polymers and at least a 90% agreement for those of 6 and 8-block polymers. We present fronts for 10-block polymer that are not possible to obtain by direct enumeration. We note that our Monte Carlo methods also return polymers close to the predicted front and a measure of the closeness. Both quantities are useful information for the design and discovery of new polymers.},
doi = {10.1016/j.commatsci.2016.08.018},
journal = {Computational Materials Science},
number = C,
volume = 125,
place = {United States},
year = {Sat Sep 03 00:00:00 EDT 2016},
month = {Sat Sep 03 00:00:00 EDT 2016}
}
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