Theory Aware Machine Learning (TaML) by US Government | Technology and IT

About this Dataset

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.
Organization: National Institute of Standards and Technology
Last updated: 2022-06-22T02:06:09.844892
Tags: machine-learning, polymers, theory, transfer-learning

Tables

Table 1

@usgov.national_institute_of_standard_theory_aware_machine_le_e3d00738.table_1

3.24 KB
10 rows
3 columns


CREATE TABLE table_1 (
  "n__data_size" DOUBLE,
  "n__mse" DOUBLE,
  "n__mse_std_error" DOUBLE
);