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

Journal Article · Sat Sep 03 00:00:00 EDT 2016 · Computational Materials Science

DOI:https://doi.org/10.1016/j.commatsci.2016.08.018· OSTI ID:1459808

Mannodi-Kanakkithodi, Arun ^[1]; Pilania, Ghanshyam  ^[2]; Ramprasad, Rampi ^[1]; Lookman, Turab  ^[2]; Gubernatis, James E.  ^[2]

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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)

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.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program; National Science Foundation (NSF). Extreme Science and Engineering Discovery Environment (XSEDE)
• Grant/Contract Number: AC52-06NA25396
• OSTI ID: 1459808
• Alternate ID(s): OSTI ID: 1402407
• Report Number(s): LA-UR-16-25544; TRN: US1901803
• Journal Information: Computational Materials Science, Vol. 125, Issue C; ISSN 0927-0256
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

36 MATERIALS SCIENCE
97 MATHEMATICS AND COMPUTING
Materials informatics
Density functional theory
Multi-objective optimization