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https://github.com/mpds-io/mpds-api

Tutorials, notebooks, issue tracker, and website on the MPDS API: the data retrieval interface for the Materials Platform for Data Science
https://github.com/mpds-io/mpds-api

calphad crystal-structure crystallography data-science materials materials-informatics materials-platform materials-science mpds-api mpds-platform phase-diagram phase-diagrams

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Tutorials, notebooks, issue tracker, and website on the MPDS API: the data retrieval interface for the Materials Platform for Data Science

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README 

        
Materials Platform for Data Science: API

==========



The API stands for the *application programming interface*, a way to get the MPDS scientific data automatically in a high-throughput manner for the machine analysis. The possible applications are high-throughput simulations, machine learning, and other *data-intensive* techniques in materials science.



![MPDS: Materials Platform for Data Science](https://raw.githubusercontent.com/mpds-io/mpds-api/gh-pages/figures/materials_platform_for_data_science.png "MPDS: Materials Platform for Data Science")



Here you will find:



- website [developer.mpds.io](https://developer.mpds.io) with the documentation

- issue tracker for the MPDS API (please, report any troubles [creating issues](https://github.com/mpds-io/mpds-api/issues))

- kickoff Python scripts:



    - [The uranium-oxygen chemical bond length distribution](https://github.com/mpds-io/mpds-api/blob/gh-pages/kickoff/miner_bondlength.py)

    - [Clustering the band gaps of binary compounds](https://github.com/mpds-io/mpds-api/blob/gh-pages/kickoff/miner_bgkmeans.py)

    - [Statistical relationship of physical property and crystalline structure](https://github.com/mpds-io/mpds-api/blob/gh-pages/kickoff/miner_propstruct.py)

    - [Retrieval of binary systems producing no compounds](https://github.com/mpds-io/mpds-api/blob/gh-pages/kickoff/miner_nonformers.py)

    - [Calculating the Pilling-Bedworth ratio of metals](https://github.com/mpds-io/mpds-api/blob/gh-pages/kickoff/miner_pb_ratio.py)

    - and more, just see the [MPDS API kickoff scripts](https://github.com/mpds-io/mpds-api/tree/gh-pages/kickoff) folder



- MPDS API Jupyter notebooks: [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/mpds-io/mpds-api/gh-pages?filepath=notebooks)



    - [Short intro: basic plotting using the periodic table](https://github.com/mpds-io/mpds-api/blob/gh-pages/notebooks/1_plot_pn_vs_eneg.ipynb)

    - [Basic MPDS API usage: machine-learning and peer-reviewed data](https://github.com/mpds-io/mpds-api/blob/gh-pages/notebooks/2_mpds_basic.ipynb)

    - [Advanced MPDS API usage: unusual materials phases from the machine learning](https://github.com/mpds-io/mpds-api/blob/gh-pages/notebooks/3_mpds_ml_scan.ipynb)

    - [Advanced MPDS API usage: pVT-data and EoS fitting](https://github.com/mpds-io/mpds-api/blob/gh-pages/notebooks/4_eos_fit.ipynb)



All information here is freely available under the [MIT](https://en.wikipedia.org/wiki/MIT_License) and [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) licenses.



[Login at the MPDS](https://mpds.io/#modal/login) if you'd like to use this API with the **open** MPDS data:



- `cell parameters vs. temperature and pressure diagrams` (about 6k entries)

- `all compounds containing both Ag and K` (about 250 entries)

- `all binary compounds of oxygen` (about 6k entries)

- `all data generated by machine-learning` (about 900k entries)

- `all data generated by first-principles calculations`



Contact us at  if you'd like to use this API with the **all** MPDS data.