{"id":18431353,"url":"https://github.com/mpds-io/mpds-api","last_synced_at":"2026-01-30T22:36:34.900Z","repository":{"id":45032164,"uuid":"74370730","full_name":"mpds-io/mpds-api","owner":"mpds-io","description":"Tutorials, notebooks, issue tracker, and website on the MPDS API: the data retrieval interface for the Materials Platform for Data Science","archived":false,"fork":false,"pushed_at":"2025-08-26T19:34:37.000Z","size":1231,"stargazers_count":26,"open_issues_count":11,"forks_count":5,"subscribers_count":2,"default_branch":"gh-pages","last_synced_at":"2025-08-27T03:42:03.150Z","etag":null,"topics":["calphad","crystal-structure","crystallography","data-science","materials","materials-informatics","materials-platform","materials-science","mpds-api","mpds-platform","phase-diagram","phase-diagrams"],"latest_commit_sha":null,"homepage":"https://developer.mpds.io","language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mpds-io.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2016-11-21T14:17:47.000Z","updated_at":"2025-08-26T19:34:41.000Z","dependencies_parsed_at":"2023-09-27T08:47:13.337Z","dependency_job_id":"4c39d67c-abf3-4197-b945-126856d8ea10","html_url":"https://github.com/mpds-io/mpds-api","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/mpds-io/mpds-api","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mpds-io%2Fmpds-api","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mpds-io%2Fmpds-api/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mpds-io%2Fmpds-api/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mpds-io%2Fmpds-api/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mpds-io","download_url":"https://codeload.github.com/mpds-io/mpds-api/tar.gz/refs/heads/gh-pages","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mpds-io%2Fmpds-api/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28921390,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-30T22:32:35.345Z","status":"ssl_error","status_checked_at":"2026-01-30T22:32:31.927Z","response_time":66,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["calphad","crystal-structure","crystallography","data-science","materials","materials-informatics","materials-platform","materials-science","mpds-api","mpds-platform","phase-diagram","phase-diagrams"],"created_at":"2024-11-06T05:24:31.715Z","updated_at":"2026-01-30T22:36:34.878Z","avatar_url":"https://github.com/mpds-io.png","language":"HTML","funding_links":[],"categories":["Datasets"],"sub_categories":[],"readme":"Materials Platform for Data Science: API\n==========\n\nThe 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.\n\n![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\")\n\nHere you will find:\n\n- website [developer.mpds.io](https://developer.mpds.io) with the documentation\n- issue tracker for the MPDS API (please, report any troubles [creating issues](https://github.com/mpds-io/mpds-api/issues))\n- kickoff Python scripts:\n\n    - [The uranium-oxygen chemical bond length distribution](https://github.com/mpds-io/mpds-api/blob/gh-pages/kickoff/miner_bondlength.py)\n    - [Clustering the band gaps of binary compounds](https://github.com/mpds-io/mpds-api/blob/gh-pages/kickoff/miner_bgkmeans.py)\n    - [Statistical relationship of physical property and crystalline structure](https://github.com/mpds-io/mpds-api/blob/gh-pages/kickoff/miner_propstruct.py)\n    - [Retrieval of binary systems producing no compounds](https://github.com/mpds-io/mpds-api/blob/gh-pages/kickoff/miner_nonformers.py)\n    - [Calculating the Pilling-Bedworth ratio of metals](https://github.com/mpds-io/mpds-api/blob/gh-pages/kickoff/miner_pb_ratio.py)\n    - and more, just see the [MPDS API kickoff scripts](https://github.com/mpds-io/mpds-api/tree/gh-pages/kickoff) folder\n\n- MPDS API Jupyter notebooks: [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/mpds-io/mpds-api/gh-pages?filepath=notebooks)\n\n    - [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)\n    - [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)\n    - [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)\n    - [Advanced MPDS API usage: pVT-data and EoS fitting](https://github.com/mpds-io/mpds-api/blob/gh-pages/notebooks/4_eos_fit.ipynb)\n\nAll 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.\n\n[Login at the MPDS](https://mpds.io/#modal/login) if you'd like to use this API with the **open** MPDS data:\n\n- `cell parameters vs. temperature and pressure diagrams` (about 6k entries)\n- `all compounds containing both Ag and K` (about 250 entries)\n- `all binary compounds of oxygen` (about 6k entries)\n- `all data generated by machine-learning` (about 900k entries)\n- `all data generated by first-principles calculations`\n\nContact us at \u003cmpds-api@tilde.pro\u003e if you'd like to use this API with the **all** MPDS data.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmpds-io%2Fmpds-api","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmpds-io%2Fmpds-api","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmpds-io%2Fmpds-api/lists"}