pymks-clean
Science Score: 54.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
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✓DOI references
Found 8 DOI reference(s) in README -
○Academic publication links
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✓Committers with academic emails
8 of 16 committers (50.0%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (11.4%) to scientific vocabulary
Keywords from Contributors
Repository
Basic Info
- Host: GitHub
- Owner: wd15
- License: other
- Language: Jupyter Notebook
- Default Branch: master
- Size: 44.6 MB
Statistics
- Stars: 1
- Watchers: 3
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Overview
MKS
The Materials Knowledge Systems (MKS) is a novel data science approach for solving multiscale materials science problems. It uses techniques from physics, machine learning, regression analysis, signal processing, and spatial statistics to create processing-structure-property relationships. The MKS carries the potential to bridge multiple length scales using localization and homogenization linkages, and provides a data driven framework for solving inverse material design problems.
See these references for further reading:
Computationally-Efficient Fully-Coupled Multi-Scale Modeling of Materials Phenomena Using Calibrated Localization Linkages, S. R. Kalidindi; ISRN Materials Science, vol. 2012, Article ID 305692, 2012, doi:10.5402/2012/305692.
Formulation and Calibration of Higher-Order Elastic Localization Relationships Using the MKS Approach, Tony Fast and S. R. Kalidindi; Acta Materialia, vol. 59 (11), pp. 4595-4605, 2011, doi:10.1016/j.actamat.2011.04.005
Developing higher-order materials knowledge systems, T. N. Fast; Thesis (PhD, Materials engineering)--Drexel University, 2011, doi:1860/4057.
PyMKS
The Materials Knowledge Materials in Python (PyMKS) framework is an object-oriented set of tools and examples, written in Python, that provide high-level access to the MKS framework for rapid creation and analysis of structure-property-processing relationships. A short introduction to how to use PyMKS is outlined below and example cases can be found in the examples section. Both code and examples contributions are welcome.
Mailing List
Please feel free to ask open-ended questions about PyMKS on the pymks-general@googlegroups.com list.
Owner
- Name: Daniel Wheeler
- Login: wd15
- Kind: user
- Location: Gaithersburg, MD
- Company: NIST
- Website: http://wd15.github.io/
- Repositories: 39
- Profile: https://github.com/wd15
Interested in the development and deployment of software for applied scientific applications. A developer for FiPy and PyMKS.
Citation (CITATION.md)
# Citing To cite PyMKS, please use the following citation: Wheeler, Daniel; Brough, David; Fast, Tony; Kalidindi, Surya; Reid, Andrew (2014): PyMKS: Materials Knowledge System in Python. figshare. http://dx.doi.org/10.6084/m9.figshare.1015761
GitHub Events
Total
Last Year
Committers
Last synced: about 1 year ago
Top Committers
| Name | Commits | |
|---|---|---|
| David Brough | d****6@g****m | 369 |
| Daniel Wheeler | d****2@g****m | 220 |
| Alicia White | a****0@g****u | 9 |
| Fred Hohman | f****n@g****m | 9 |
| alohse | a****3@g****u | 8 |
| Noah Paulson | n****n@g****m | 5 |
| aiskakov | a****6@g****m | 5 |
| epopova | e****a@i****u | 5 |
| Surya Kalidindi | s****i@m****u | 3 |
| Ahmet Cecen | a****n | 3 |
| Andrew Castillo | j****7@g****u | 2 |
| AJ Medford | a****r@g****m | 1 |
| Aleksandr Blekh | a****h@g****u | 1 |
| soumyamohan10 | a****a@g****m | 1 |
| epopova | e****a@i****u | 1 |
| epopova | e****a@i****u | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: about 1 year ago
All Time
- Total issues: 0
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 0
- Total pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0