• Login
    View Item 
    •   DSpace@RPI Home
    • Tetherless World Constellation
    • Tetherless World Publications
    • View Item
    •   DSpace@RPI Home
    • Tetherless World Constellation
    • Tetherless World Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Data centric nanocomposites design via mixed-variable Bayesian optimization

    Author
    Iyer, Akshay; Zhang, Yichi; Prasad, Aditya; Gupta, Praveen; Tao, Siyu; Wang, Yixian; Prabhune, Prajakta; Schadler, Linda; Brinson, Cate; Chen, Wei
    Thumbnail
    Other Contributors
    Date Issued
    2020-08-10
    Subject
    MaterialsMine: An open-source, user-friendly materials data resource guided by FAIR principles
    Degree
    Terms of Use
    Metadata
    Show full item record
    URI
    http://dx.doi.org/10.1039/D0ME00079E
    Abstract
    With an unprecedented combination of mechanical and electrical properties, polymer nanocomposites have the potential to be widely used across multiple industries. Tailoring nanocomposites to meet application specific requirements remains a challenging task, owing to the vast, mixed-variable design space that includes composition (i.e. choice of polymer, nanoparticle, and surface modification) and microstructures (i.e. dispersion and geometric arrangement of particles) of the nanocomposite material. Modeling properties of the interphase, the region surrounding a nanoparticle, introduces additional complexity to the design process and requires computationally expensive simulations. As a result, previous attempts at designing polymer nanocomposites have focused on finding the optimal microstructure for only a fixed combination of constituents. In this article, we propose a data centric design framework to concurrently identify optimal composition and microstructure using mixed-variable Bayesian optimization. This framework integrates experimental data with state-of-the-art techniques in interphase modeling, microstructure characterization and reconstructions and machine learning. Latent variable Gaussian processes (LVGPs) quantifies the lack-of-data uncertainty over the mixed-variable design space that consists of qualitative and quantitative material design variables. The design of electrically insulating nanocomposites is cast as a multicriteria optimization problem with the goal of maximizing dielectric breakdown strength while minimizing dielectric permittivity and dielectric loss. Within tens of simulations, our method identifies a diverse set of designs on the Pareto frontier indicating the tradeoff between dielectric properties. These findings project data centric design, effectively integrating experimental data with simulations for Bayesian Optimization, as an effective approach for design of engineered material systems.;
    Description
    pages 1376 - 1390
    Department
    Publisher
    Molecular Systems Design & Engineering
    Relationships
    https://tw.rpi.edu/project/materialsmine-open-source-user-friendly-materials-data-resource-guided-fair-principles;
    Access
    Collections
    • Tetherless World Publications

    Browse

    All of DSpace@RPICommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    Login

    DSpace software copyright © 2002-2022  DuraSpace
    Contact Us | Send Feedback
    DSpace Express is a service operated by 
    Atmire NV