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 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](https://doi.org/10.1021/acsmacrolett.2c00369).

[
  "006:55"
]

[
  "en"
]

[
  "006:045"
]

[
  "https://dx.doi.org/10.1021/acsmacrolett.2c00369"
]

[
  "Information Technology:Data and informatics",
  "Materials:Modeling and computational material science",
  "Materials:Polymers",
  "Mathematics and Statistics:Uncertainty quantification"
]