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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 associated with the data. For additional details on the code, please refer to the 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



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

Metadata Source

Harvested from NIST

Additional Metadata

Resource Type Dataset
Metadata Created Date June 22, 2022
Metadata Updated Date January 7, 2023
Publisher National Institute of Standards and Technology
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
Schema Version
Catalog Describedby
Harvest Object Id b38d5b1a-b966-4b4c-bf53-a2624859d6d8
Harvest Source Id 74e175d9-66b3-4323-ac98-e2a90eeb93c0
Harvest Source Title NIST
Homepage URL
Program Code 006:045
Related Documents
Source Datajson Identifier True
Source Hash c46fe661f586d7963c568ded87920e14f09c590d
Source Schema Version 1.1

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