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| Name: | MAST-ML |
| Description: | This project will develop an approach to accelerate the entire machine learning workflow. Its output will include tools to easily develop datasets, manage model development, and output models. These will be reusable and reproducible for future use. This project will enable materials scientists and engineers to rapidly develop and deploy machine learning models. |
| Type: | Workflow Management |
| Keywords: | ML Models , ML workflow, CyberInfrastructure: MAterials Simulation Toolkit for Machine Learning (MAST-ML) |
| License: | MIT |
| Project URL: | |
| Download URL: | https://github.com/uw-cmg/MAST-ML |
| Installation URL: | https://mastmldocs.readthedocs.io/en/latest/ |
| Software Stack: | Python |
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| Acknowledgement: | |
| Funding Agency: | NSF |
| Award No: | 1931306, 1931298 |
| Funded Project Title: | Collaborative Research: Framework: Machine Learning Materials Innovation Infrastructure |
| PI Name: | Benjamin Blaiszik, Dane Morgan, Ryan Jacobs |
| PI Email: | blaiszik@uchicago.edu |
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| Metric Name | Count | ||
|---|---|---|---|
| github_commits | 3312 | ||
| github_contributors | 18 | ||
| github_forks | 61 | ||
| github_stars | 108 |