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Title: Scoping the polymer genome: A roadmap for rational polymer dielectrics design and beyond

Journal Article · · Materials Today
 [1];  [2];  [2];  [2]; ORCiD logo [3]; ORCiD logo [4];  [2]
  1. Univ. of Connecticut, Storrs, CT (United States); Argonne National Lab. (ANL), Lemont, IL (United States)
  2. Univ. of Connecticut, Storrs, CT (United States)
  3. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin (Germany)
  4. Corning Research & Development Corp., Corning, NY (United States)

The Materials Genome Initiative (MGI) has heralded a sea change in the philosophy of materials design. In an increasing number of applications, the successful deployment of novel materials has benefited from the use of computational methodologies, data descriptors, and machine learning. Polymers have long suffered from a lack of data on electronic, mechanical, and dielectric properties across large chemical spaces, causing a stagnation in the set of suitable candidates for various applications. Extensive efforts over the last few years have seen the fruitful application of MGI principles toward the accelerated discovery of attractive polymer dielectrics for capacitive energy storage. Here, we review these efforts, highlighting the importance of computational data generation and screening, targeted synthesis and characterization, polymer fingerprinting and machine-learning prediction models, and the creation of an online knowledgebase to guide ongoing and future polymer discovery and design. We lay special emphasis on the fingerprinting of polymers in terms of their genome or constituent atomic and molecular fragments, an idea that pays homage to the pioneers of the human genome project who identified the basic building blocks of the human DNA. By scoping the polymer genome, we present an essential roadmap for the design of polymer dielectrics, and provide future perspectives and directions for expansions to other polymer subclasses and properties.

Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Organization:
Universities/Institutions; USDOE; US Department of the Navy, Office of Naval Research (ONR); Alexander von Humboldt Foundation
Grant/Contract Number:
AC52-06NA25396; AC02-06CH11357
OSTI ID:
1415426
Alternate ID(s):
OSTI ID: 1542587
Report Number(s):
LA-UR-17-29595; TRN: US1800819
Journal Information:
Materials Today, Vol. 21, Issue 7; ISSN 1369-7021
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 111 works
Citation information provided by
Web of Science

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Cited By (17)

Machine-learned impurity level prediction for semiconductors: the example of Cd-based chalcogenides journal April 2020
Active-learning and materials design: the example of high glass transition temperature polymers journal June 2019
Soft Matter Informatics: Current Progress and Challenges journal November 2018
Machine learning enables polymer cloud-point engineering via inverse design journal July 2019
Solving the electronic structure problem with machine learning journal February 2019
Facile Synthesis of Poly(Glycidyl Ether)s with Ionic Pendant Groups by Thiol‐Ene Reactions journal October 2019
Designing promising molecules for organic solar cells via machine learning assisted virtual screening journal January 2019
Effect of Constituent Materials on Composite Performance: Exploring Design Strategies via Machine Learning journal April 2019
Pressure-stabilized binary compounds of magnesium and silicon journal February 2018
Emerging role of machine learning in light-matter interaction journal September 2019
Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges journal January 2020
Review of Polymer‐Based Nanodielectric Exploration and Film Scale‐Up for Advanced Capacitors journal April 2019
Silicon‐containing fluorenylacetylene resins with low curing temperature and high thermal stability journal July 2019
Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm journal June 2019
Sn‐Polyester/Polyimide Hybrid Flexible Free‐Standing Film as a Tunable Dielectric Material journal November 2018
Predicting Materials Properties with Little Data Using Shotgun Transfer Learning journal September 2019
Off-the-shelf deep learning is not enough: parsimony, Bayes and causality preprint January 2020

Figures / Tables (11)


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