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Theory aware Machine Learning (TaML)

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 a forthcoming manuscript titled "Leveraging theory for enhanced machine learning," by Debra J. Audus, Austin McDannald and Brian DeCost.

About this Dataset

Updated: 2022-07-29
Metadata Last Updated: 2022-05-06
Date Created: N/A
Views:
Data Provided by:
National Institute of Standards and Technology
Dataset Owner: N/A

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Table representation of structured data
Title Theory aware Machine Learning (TaML)
Description 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 a forthcoming manuscript titled "Leveraging theory for enhanced machine learning," by Debra J. Audus, Austin McDannald and Brian DeCost.
Modified N/A
Publisher Name National Institute of Standards and Technology
Contact mailto:debra.audus@nist.gov
Keywords polymers , machine learning , transfer learning , theory
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    "license": "https:\/\/www.nist.gov\/open\/license",
    "bureauCode": [
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    "modified": "2022-05-06 00:00:00",
    "publisher": {
        "@type": "org:Organization",
        "name": "National Institute of Standards and Technology"
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    "accrualPeriodicity": "irregular",
    "theme": [
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        "Materials:Modeling and computational material science",
        "Information Technology:Data and informatics",
        "Materials:Polymers"
    ],
    "keyword": [
        "polymers",
        "machine learning",
        "transfer learning",
        "theory"
    ]
}

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