uw-cmg/materials_application_domain_machine_learning

A package for definining the domain of a machine learning model via a feature dissimilarity metric.

Materials Application Domain Machine Learning (MADML)

Research with respect to application domain with a materials science emphasis is contained within. The GitHub repo can be found in here.

Examples

• Tutorial 1: Assess and fit a single model from all data:
• Tutorial 2: Use model hosted on Docker Hub:

Structure

The structure of the code packages is as follows:

materials_application_domain_machine_learning/
├── examples
│   ├── jupyter
│   └── single_runs
├── src
│   └── madml
└── tests

Coding Style

Python scripts follow PEP 8 guidelines. A usefull tool to use to check a coding style is pycodestyle.

pycodestyle <script>

Authors

Graduate Students

• Lane Schultz - Main Contributer - leschultz

Acknowledgments

• The Computational Materials Group (CMG) at the University of Wisconsin - Madison
• Professor Dane Morgan ddmorgan and Dr. Ryan Jacobs rjacobs914 for computational material science guidence