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
Metadata
Release files for madml 2.6.7
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| madml-2.6.7.tar.gz | 18.8 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| madml-2.6.7-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 37.7 MB
Release files / madml-2.6.7.tar.gz
| Download URL | madml-2.6.7.tar.gz |
|---|---|
| Size | 18.8 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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twine/5.1.0 CPython/3.12.4
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Release files / madml-2.6.7-py3-none-any.whl
| Download URL | madml-2.6.7-py3-none-any.whl |
|---|---|
| Size | 18.8 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/5.1.0 CPython/3.12.4
|