jarvis-leaderboard


Namejarvis-leaderboard JSON
Version 2024.4.26 PyPI version JSON
download
home_pagehttps://github.com/knc6/jarvis_leaderboard
Summaryjarvis_leaderboard
upload_time2024-05-16 16:33:19
maintainerNone
docs_urlNone
authorKamal Choudhary
requires_python>=3.8
licenseNone
keywords
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            ![Leaderboard actions](https://github.com/usnistgov/jarvis_leaderboard/actions/workflows/test_build.yml/badge.svg)
![GitHub repo size](https://img.shields.io/github/repo-size/usnistgov/jarvis_leaderboard)
[![name](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Upload_benchmark_to_jarvis_leaderboard.ipynb)
[![name](https://colab.research.google.com/assets/colab-badge.svg)]([https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Upload_benchmark_to_jarvis_leaderboard.ipynb](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/alignn_jarvis_leaderboard.ipynb))
[![Downloads](https://pepy.tech/badge/jarvis_leaderboard)](https://pepy.tech/project/jarvis_leaderboard)
[![DOI](https://zenodo.org/badge/514340921.svg)](https://zenodo.org/badge/latestdoi/514340921)



# JARVIS-Leaderboard:

This project provides benchmark-performances of various methods for materials science applications using the datasets available in JARVIS-Tools databases. Some of the methods are: Artificial Intelligence (AI), Electronic Structure (ES), Force-field (FF), Qunatum Computation (QC) and Experiments (EXP). There are a variety of properties included in the benchmark. In addition to prediction results, we attempt to capture the underlyig software, hardware and instrumental frameworks to enhance reproducibility. This project is a part of the NIST-JARVIS infrastructure.

Website: https://pages.nist.gov/jarvis_leaderboard/


            

Raw data

            {
    "_id": null,
    "home_page": "https://github.com/knc6/jarvis_leaderboard",
    "name": "jarvis-leaderboard",
    "maintainer": null,
    "docs_url": null,
    "requires_python": ">=3.8",
    "maintainer_email": null,
    "keywords": null,
    "author": "Kamal Choudhary",
    "author_email": "kamal.choudhary@nist.gov",
    "download_url": "https://files.pythonhosted.org/packages/41/f0/ff5eb7218c73b9ed68d6f25c340dd9a07085274d2ab166cc0f992af6e2e4/jarvis_leaderboard-2024.4.26.tar.gz",
    "platform": null,
    "description": "![Leaderboard actions](https://github.com/usnistgov/jarvis_leaderboard/actions/workflows/test_build.yml/badge.svg)\n![GitHub repo size](https://img.shields.io/github/repo-size/usnistgov/jarvis_leaderboard)\n[![name](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Upload_benchmark_to_jarvis_leaderboard.ipynb)\n[![name](https://colab.research.google.com/assets/colab-badge.svg)]([https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Upload_benchmark_to_jarvis_leaderboard.ipynb](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/alignn_jarvis_leaderboard.ipynb))\n[![Downloads](https://pepy.tech/badge/jarvis_leaderboard)](https://pepy.tech/project/jarvis_leaderboard)\n[![DOI](https://zenodo.org/badge/514340921.svg)](https://zenodo.org/badge/latestdoi/514340921)\n\n\n\n# JARVIS-Leaderboard:\n\nThis project provides benchmark-performances of various methods for materials science applications using the datasets available in JARVIS-Tools databases. Some of the methods are: Artificial Intelligence (AI), Electronic Structure (ES), Force-field (FF), Qunatum Computation (QC) and Experiments (EXP). There are a variety of properties included in the benchmark. In addition to prediction results, we attempt to capture the underlyig software, hardware and instrumental frameworks to enhance reproducibility. This project is a part of the NIST-JARVIS infrastructure.\n\nWebsite: https://pages.nist.gov/jarvis_leaderboard/\n\n",
    "bugtrack_url": null,
    "license": null,
    "summary": "jarvis_leaderboard",
    "version": "2024.4.26",
    "project_urls": {
        "Homepage": "https://github.com/knc6/jarvis_leaderboard"
    },
    "split_keywords": [],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "fe1754f7f8255c18a001d43d1644255e369960ac0f3f4cc301487db2af6fd507",
                "md5": "4e761df934ef764ab1801acdc957170a",
                "sha256": "3da2e8127f6c980f7f18f60f093cf6d5ae09a17e59882e9aabafcc0240718df9"
            },
            "downloads": -1,
            "filename": "jarvis_leaderboard-2024.4.26-py2.py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "4e761df934ef764ab1801acdc957170a",
            "packagetype": "bdist_wheel",
            "python_version": "py2.py3",
            "requires_python": ">=3.8",
            "size": 72054000,
            "upload_time": "2024-05-16T16:33:11",
            "upload_time_iso_8601": "2024-05-16T16:33:11.236532Z",
            "url": "https://files.pythonhosted.org/packages/fe/17/54f7f8255c18a001d43d1644255e369960ac0f3f4cc301487db2af6fd507/jarvis_leaderboard-2024.4.26-py2.py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "41f0ff5eb7218c73b9ed68d6f25c340dd9a07085274d2ab166cc0f992af6e2e4",
                "md5": "df0c0209e04f13d4cb0dfff3db8cf3dc",
                "sha256": "5041aea197a5ef031dc059788a895b4fc0d877ac38ffa99ad6ae854d572c88e9"
            },
            "downloads": -1,
            "filename": "jarvis_leaderboard-2024.4.26.tar.gz",
            "has_sig": false,
            "md5_digest": "df0c0209e04f13d4cb0dfff3db8cf3dc",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.8",
            "size": 71958913,
            "upload_time": "2024-05-16T16:33:19",
            "upload_time_iso_8601": "2024-05-16T16:33:19.096891Z",
            "url": "https://files.pythonhosted.org/packages/41/f0/ff5eb7218c73b9ed68d6f25c340dd9a07085274d2ab166cc0f992af6e2e4/jarvis_leaderboard-2024.4.26.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2024-05-16 16:33:19",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "knc6",
    "github_project": "jarvis_leaderboard",
    "travis_ci": false,
    "coveralls": false,
    "github_actions": true,
    "lcname": "jarvis-leaderboard"
}
        
Elapsed time: 0.31888s