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    • Theory aware Machine Learning (TaML)

    Metadata Updated: January 7, 2023

    A code repository and accompanying data for incorporating imperfect theory into machine learning for improved prediction and explainability. Specifically, it focuses on the case study of the dimensions of a polymer chain in different solvent qualities. Jupyter Notebooks for quickly testing concepts and reproducing figures, as well as source code that computes the mean squared error as a function of dataset size for various machine learning models are included.For additional details on the data, please refer to the README.md associated with the data. For additional details on the code, please refer to the README.md provided with the code repository (GitHub Repo for Theory aware Machine Learning). For additional details on the methodology, see Debra J. Audus, Austin McDannald, and Brian DeCost, "Leveraging Theory for Enhanced Machine Learning" ACS Macro Letters 2022 11 (9), 1117-1122 DOI: 10.1021/acsmacrolett.2c00369.

    Access & Use Information

    Public: This dataset is intended for public access and use. License: See this page for license information.

    Downloads & Resources

    References

    https://dx.doi.org/10.1021/acsmacrolett.2c00369

    Dates

    Metadata Created Date June 22, 2022
    Metadata Updated Date January 7, 2023

    Metadata Source

    Harvested from NIST

    Other Data Resources

    Additional Metadata

    Resource Type Dataset
    Metadata Created Date June 22, 2022
    Metadata Updated Date January 7, 2023
    Publisher National Institute of Standards and Technology
    Maintainer
    Identifier ark:/88434/mds2-2637
    Language en
    Data Last Modified 2022-05-06 00:00:00
    Category Materials:Polymers, Information Technology:Data and informatics, Materials:Modeling and computational material science, Mathematics and Statistics:Uncertainty quantification
    Public Access Level public
    Bureau Code 006:55
    Metadata Context https://project-open-data.cio.gov/v1.1/schema/data.json
    Schema Version https://project-open-data.cio.gov/v1.1/schema
    Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
    Harvest Object Id b38d5b1a-b966-4b4c-bf53-a2624859d6d8
    Harvest Source Id 74e175d9-66b3-4323-ac98-e2a90eeb93c0
    Harvest Source Title NIST
    Homepage URL https://data.nist.gov/od/id/mds2-2637
    License https://www.nist.gov/open/license
    Program Code 006:045
    Related Documents https://dx.doi.org/10.1021/acsmacrolett.2c00369
    Source Datajson Identifier True
    Source Hash c46fe661f586d7963c568ded87920e14f09c590d
    Source Schema Version 1.1

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