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Title: Multi-objective optimization techniques to design the Pareto front of organic dielectric polymers

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 » the closeness. Both quantities are useful information for the design and discovery of new polymers.« less
Authors:
 [1] ; ORCiD logo [2] ;  [1] ; ORCiD logo [2] ; ORCiD logo [2]
  1. Univ. of Connecticut, Storrs, CT (United States). Dept. of Materials Science and Engineering and Inst. of Materials Science
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Report Number(s):
LA-UR-16-25544
Journal ID: ISSN 0927-0256
Grant/Contract Number:
AC52-06NA25396
Type:
Accepted Manuscript
Journal Name:
Computational Materials Science
Additional Journal Information:
Journal Volume: 125; Journal Issue: C; Journal ID: ISSN 0927-0256
Publisher:
Elsevier
Research Org:
Los Alamos National Lab. (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)
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; 97 MATHEMATICS AND COMPUTING; Materials informatics; Density functional theory; Multi-objective optimization
OSTI Identifier:
1459808
Alternate Identifier(s):
OSTI ID: 1402407