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    • Theory aware Machine Learning (TaML)

    Metadata Updated: September 30, 2025

    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 September 30, 2025
    Metadata Updated Date September 30, 2025

    Metadata Source

    Harvested from Commerce Non Spatial Data.json Harvest Source

    Other Data Resources

    Additional Metadata

    Resource Type Dataset
    Metadata Created Date September 30, 2025
    Metadata Updated Date September 30, 2025
    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/catalog.jsonld
    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 5b2440ba-9a42-4f58-b105-065da433dba6
    Harvest Source Id bce99b55-29c1-47be-b214-b8e71e9180b1
    Harvest Source Title Commerce Non Spatial Data.json Harvest Source
    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 ec0abd23b20fdbb77f183b8126459662858f4f19b30d8302708143e34c5aff26
    Source Schema Version 1.1

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