quansino


Namequansino JSON
Version 0.0.1 PyPI version JSON
download
home_pageNone
Summary`quansino` is a modular package based on the Atomic Simulation Environment (ASE) for quickly building custom Monte Carlo algorithms
upload_time2025-01-07 22:34:20
maintainerNone
docs_urlNone
authorNone
requires_python>=3.10
licenseBSD-3
keywords monte carlo computational chemistry quantum chemistry
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            <div align="center">
  <img src=docs/images/quansino_logo.png width="500"><br>
</div>

<div align="center">
  <h1><code>quansino</code> :game_die:</h1>
  <p><i>Modular Stochastic Simulations for Atomistic Modelling</i></p>
</div>

***

[![PyPI version](https://badge.fury.io/py/quansino.svg)](https://badge.fury.io/py/quansino)
![Python Version](https://img.shields.io/pypi/pyversions/quansino)
[![codecov](https://codecov.io/gh/Atomic-Samplers/quansino/branch/main/graph/badge.svg?token=A864UNYUOG)](https://codecov.io/gh/Atomic-Samplers/quansino)
[![GitHub license](https://img.shields.io/github/license/Atomic-Samplers/quansino)](https://github.com/Atomic-Samplers/quansino/blob/main/LICENSE.md)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit)](https://github.com/pre-commit/pre-commit)

`quansino` is a modern simulation framework based on the Atomic Simulation Environment (ASE) designed for both material science and molecular systems. It combines the reliability of established Monte Carlo and general sampling methods with an intuitive, and flexible Python interface.

## Key Features :slot_machine:

- Perform (grand) canonical, isobaric, and other ensemble simulations. The framework allows designing custom ensembles and custom parametrization for subsystems or degrees of freedom.
- Include algorithm for efficient sampling of complex energy landscapes, such as basin-hopping, and force-biased Monte Carlo.
- Being based on ASE, `quansino` supports a wide range of DFT codes and force fields.

## Documentation :8ball:

The full documentation can be found [here](https://atomic-samplers.github.io/quansino/), and includes detailed instructions about:

- :gear: [Installation](https://atomic-samplers.github.io/quansino/installation/install.html)
- :eyes: [Overview](https://atomic-samplers.github.io/quansino/documentation/overview.html)
- :thought_balloon: [Examples](https://atomic-samplers.github.io/quansino/documentation/examples.html)

## License :black_joker:

This project is licensed under the terms of the BSD 3-Clause license.

            

Raw data

            {
    "_id": null,
    "home_page": null,
    "name": "quansino",
    "maintainer": null,
    "docs_url": null,
    "requires_python": ">=3.10",
    "maintainer_email": null,
    "keywords": "Monte Carlo, Computational Chemistry, Quantum Chemistry",
    "author": null,
    "author_email": "Tom Demeyere <tom.dmre@gmail.com>",
    "download_url": "https://files.pythonhosted.org/packages/cb/52/d8d8cb586619f286dd56b18759fcb29aba6b7363c9dda55b9da62b7bdf61/quansino-0.0.1.tar.gz",
    "platform": null,
    "description": "<div align=\"center\">\n  <img src=docs/images/quansino_logo.png width=\"500\"><br>\n</div>\n\n<div align=\"center\">\n  <h1><code>quansino</code> :game_die:</h1>\n  <p><i>Modular Stochastic Simulations for Atomistic Modelling</i></p>\n</div>\n\n***\n\n[![PyPI version](https://badge.fury.io/py/quansino.svg)](https://badge.fury.io/py/quansino)\n![Python Version](https://img.shields.io/pypi/pyversions/quansino)\n[![codecov](https://codecov.io/gh/Atomic-Samplers/quansino/branch/main/graph/badge.svg?token=A864UNYUOG)](https://codecov.io/gh/Atomic-Samplers/quansino)\n[![GitHub license](https://img.shields.io/github/license/Atomic-Samplers/quansino)](https://github.com/Atomic-Samplers/quansino/blob/main/LICENSE.md)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit)](https://github.com/pre-commit/pre-commit)\n\n`quansino` is a modern simulation framework based on the Atomic Simulation Environment (ASE) designed for both material science and molecular systems. It combines the reliability of established Monte Carlo and general sampling methods with an intuitive, and flexible Python interface.\n\n## Key Features :slot_machine:\n\n- Perform (grand) canonical, isobaric, and other ensemble simulations. The framework allows designing custom ensembles and custom parametrization for subsystems or degrees of freedom.\n- Include algorithm for efficient sampling of complex energy landscapes, such as basin-hopping, and force-biased Monte Carlo.\n- Being based on ASE, `quansino` supports a wide range of DFT codes and force fields.\n\n## Documentation :8ball:\n\nThe full documentation can be found [here](https://atomic-samplers.github.io/quansino/), and includes detailed instructions about:\n\n- :gear: [Installation](https://atomic-samplers.github.io/quansino/installation/install.html)\n- :eyes: [Overview](https://atomic-samplers.github.io/quansino/documentation/overview.html)\n- :thought_balloon: [Examples](https://atomic-samplers.github.io/quansino/documentation/examples.html)\n\n## License :black_joker:\n\nThis project is licensed under the terms of the BSD 3-Clause license.\n",
    "bugtrack_url": null,
    "license": "BSD-3",
    "summary": "`quansino` is a modular package based on the Atomic Simulation Environment (ASE) for quickly building custom Monte Carlo algorithms",
    "version": "0.0.1",
    "project_urls": {
        "changelog": "https://github.com/Atomic-Samplers/quansino/blob/main/CHANGELOG.md",
        "documentation": "https://atomic-samplers.github.io/quansino/",
        "repository": "https://github.com/Atomic-Samplers/quansino"
    },
    "split_keywords": [
        "monte carlo",
        " computational chemistry",
        " quantum chemistry"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "cb52d8d8cb586619f286dd56b18759fcb29aba6b7363c9dda55b9da62b7bdf61",
                "md5": "dbaf62830c84e02080bbae7dae81627b",
                "sha256": "113648af15901787be0558a134b77feb778af30f1e76a95f4bb054c9b7dd89f3"
            },
            "downloads": -1,
            "filename": "quansino-0.0.1.tar.gz",
            "has_sig": false,
            "md5_digest": "dbaf62830c84e02080bbae7dae81627b",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.10",
            "size": 22896,
            "upload_time": "2025-01-07T22:34:20",
            "upload_time_iso_8601": "2025-01-07T22:34:20.234192Z",
            "url": "https://files.pythonhosted.org/packages/cb/52/d8d8cb586619f286dd56b18759fcb29aba6b7363c9dda55b9da62b7bdf61/quansino-0.0.1.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2025-01-07 22:34:20",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "Atomic-Samplers",
    "github_project": "quansino",
    "travis_ci": false,
    "coveralls": false,
    "github_actions": true,
    "lcname": "quansino"
}
        
Elapsed time: 0.40929s