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Rational design of all organic polymer dielectrics

Author

Listed:
  • Vinit Sharma

    (University of Connecticut
    Institute of Materials Science, University of Connecticut)

  • Chenchen Wang

    (University of Connecticut
    Institute of Materials Science, University of Connecticut)

  • Robert G. Lorenzini

    (Institute of Materials Science, University of Connecticut
    Institute of Materials Science, University of Connecticut)

  • Rui Ma

    (Institute of Materials Science, University of Connecticut
    Institute of Materials Science, University of Connecticut)

  • Qiang Zhu

    (Stony Brook University)

  • Daniel W. Sinkovits

    (Columbia University)

  • Ghanshyam Pilania

    (Los Alamos National Laboratory)

  • Artem R. Oganov

    (Stony Brook University
    Moscow Institute of Physics and Technology
    Northwestern Polytechnical University)

  • Sanat Kumar

    (Columbia University)

  • Gregory A. Sotzing

    (Institute of Materials Science, University of Connecticut
    Institute of Materials Science, University of Connecticut)

  • Steven A. Boggs

    (Institute of Materials Science, University of Connecticut)

  • Rampi Ramprasad

    (University of Connecticut
    Institute of Materials Science, University of Connecticut)

Abstract

To date, trial and error strategies guided by intuition have dominated the identification of materials suitable for a specific application. We are entering a data-rich, modelling-driven era where such Edisonian approaches are gradually being replaced by rational strategies, which couple predictions from advanced computational screening with targeted experimental synthesis and validation. Here, consistent with this emerging paradigm, we propose a strategy of hierarchical modelling with successive downselection stages to accelerate the identification of polymer dielectrics that have the potential to surpass ‘standard’ materials for a given application. Successful synthesis and testing of some of the most promising identified polymers and the measured attractive dielectric properties (which are in quantitative agreement with predictions) strongly supports the proposed approach to material selection.

Suggested Citation

  • Vinit Sharma & Chenchen Wang & Robert G. Lorenzini & Rui Ma & Qiang Zhu & Daniel W. Sinkovits & Ghanshyam Pilania & Artem R. Oganov & Sanat Kumar & Gregory A. Sotzing & Steven A. Boggs & Rampi Rampras, 2014. "Rational design of all organic polymer dielectrics," Nature Communications, Nature, vol. 5(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms5845
    DOI: 10.1038/ncomms5845
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    Cited by:

    1. Rishi Gurnani & Stuti Shukla & Deepak Kamal & Chao Wu & Jing Hao & Christopher Kuenneth & Pritish Aklujkar & Ashish Khomane & Robert Daniels & Ajinkya A. Deshmukh & Yang Cao & Gregory Sotzing & Rampi , 2024. "AI-assisted discovery of high-temperature dielectrics for energy storage," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    2. Yuhui Li & Guolin Wan & Yongqian Zhu & Jingyu Yang & Yan-Fang Zhang & Jinbo Pan & Shixuan Du, 2024. "High-throughput screening and machine learning classification of van der Waals dielectrics for 2D nanoelectronics," Nature Communications, Nature, vol. 15(1), pages 1-10, December.

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