{"id":23219884,"url":"https://github.com/chiang-yuan/muse","last_synced_at":"2026-04-19T01:01:44.572Z","repository":{"id":198174297,"uuid":"672107841","full_name":"chiang-yuan/muse","owner":"chiang-yuan","description":"A python package for fast building amorphous solids and liquid mixtures from @materialsproject computed structures and machine learning interatomic potentials","archived":false,"fork":false,"pushed_at":"2026-04-18T23:43:44.000Z","size":1490,"stargazers_count":7,"open_issues_count":1,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-04-19T00:39:27.391Z","etag":null,"topics":["atomistic-simulations","high-throughput","interatomic-potentials","machine-learning","materials","molecular-dynamics"],"latest_commit_sha":null,"homepage":"https://chiang-yuan.github.io/muse/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/chiang-yuan.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2023-07-29T00:36:06.000Z","updated_at":"2026-04-18T23:43:28.000Z","dependencies_parsed_at":"2025-04-12T23:44:46.319Z","dependency_job_id":null,"html_url":"https://github.com/chiang-yuan/muse","commit_stats":null,"previous_names":["chiang-yuan/muse"],"tags_count":2,"template":false,"template_full_name":null,"purl":"pkg:github/chiang-yuan/muse","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chiang-yuan%2Fmuse","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chiang-yuan%2Fmuse/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chiang-yuan%2Fmuse/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chiang-yuan%2Fmuse/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/chiang-yuan","download_url":"https://codeload.github.com/chiang-yuan/muse/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chiang-yuan%2Fmuse/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31990577,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-18T20:23:30.271Z","status":"ssl_error","status_checked_at":"2026-04-18T20:23:29.375Z","response_time":103,"last_error":"SSL_read: 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":["atomistic-simulations","high-throughput","interatomic-potentials","machine-learning","materials","molecular-dynamics"],"created_at":"2024-12-18T21:47:45.576Z","updated_at":"2026-04-19T01:01:44.557Z","avatar_url":"https://github.com/chiang-yuan.png","language":"Python","funding_links":[],"categories":["Molecular Dynamics"],"sub_categories":[],"readme":"\u003cp align=\"center\"\u003e\n  \u003cimg src=\"docs/logo.png\" alt=\"Muse logo\" width=\"200\"\u003e\n\u003c/p\u003e\n\n# Muse\n\n[![PyPI version](https://img.shields.io/pypi/v/muse-xtal.svg)](https://pypi.org/project/muse-xtal/)\n[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n[![Build and Test](https://github.com/chiang-yuan/muse/actions/workflows/test.yml/badge.svg)](https://github.com/chiang-yuan/muse/actions/workflows/test.yml)\n\n**Muse** (**M**ixture b**u**ilder for **s**imulation **e**nvironments) is a Python package for rapidly building amorphous solids and liquid mixtures from relaxed solid-state structures on [Materials Project](https://materialsproject.org/). It uses [Packmol](http://m3g.iqm.unicamp.br/packmol/home.shtml) for packing molecules into simulation cells and supports density equilibration through molecular dynamics with machine learning interatomic potentials (MLIPs), especially universal interatomic potentials (UIPs) such as [MACE](https://github.com/ACEsuit/mace) and [CHGNet](https://github.com/CederGroupHub/chgnet).\n\n## Features\n\n- **Structure generation** — Build binary/multicomponent amorphous mixtures from Materials Project crystal structures via `mix_number` and `mix_cell`\n- **Density equilibration** — Run NVT → NPT molecular dynamics workflows to compute equilibrium densities with `DensityCalc`\n- **Thermodynamic analysis** — Plot binary mixing enthalpy (G–x), density–composition, and excess volume diagrams with Redlich–Kister fits\n- **Trajectory I/O** — Convert pymatgen trajectories to extended XYZ format\n- **HPC integration** — Submit SLURM batch jobs programmatically\n\n## Installation\n\n```bash\npip install muse-xtal\n```\n\n### Optional extras\n\n```bash\n# MACE calculator support\npip install \"muse-xtal[mace]\"\n\n# Development tools (ruff, pytest)\npip install \"muse-xtal[dev]\"\n\n# Documentation building\npip install \"muse-xtal[docs]\"\n```\n\n### Prerequisites\n\nMuse requires [Packmol](http://m3g.iqm.unicamp.br/packmol/home.shtml) to be installed and available on your `PATH`. You can compile it from source:\n\n```bash\nbash scripts/install-packmol.sh\n```\n\nYou also need a [Materials Project API key](https://materialsproject.org/api) set as the `MP_API_KEY` environment variable (or in a `.env` file).\n\n## Quick Start\n\n```python\nfrom muse.transforms.mixture import mix_number\n\n# Build a NaCl–KCl mixture (3:1 ratio, ~20 atoms)\natoms = mix_number(\n    recipe={\"NaCl\": 3, \"KCl\": 1},\n    tolerance=2.0,\n    scale=1.05,\n    seed=42,\n)\nprint(atoms)  # Atoms object ready for simulation\n```\n\n### Density equilibration with MACE\n\n```python\nimport numpy as np\nfrom ase import units\nfrom mace.calculators import MACECalculator\nfrom muse.calcs.density import DensityCalc\n\ncalc = MACECalculator(model_paths=\"path/to/model\", device=\"cpu\")\n\ndensity_calc = DensityCalc(\n    calculator=calc,\n    optimizer=\"FIRE\",\n    steps=500,\n    mask=np.eye(3),\n    rtol=1e-3,\n    atol=5e-4,\n)\n\nresults = density_calc.calc(\n    atoms=atoms,\n    temperature=1100,  # K\n    externalstress=0.0,  # eV/ų\n)\nprint(f\"Density: {results['mass_density']:.4f} amu/ų\")\n```\n\n## Documentation\n\nFull documentation is available at [chiang-yuan.github.io/muse](https://chiang-yuan.github.io/muse).\n\n## Citation\n\nIf you use Muse in your research, please cite:\n\n```bibtex\n@software{chiang2023muse,\n  author    = {Chiang, Yuan},\n  title     = {muse-xtal},\n  version   = {0.2.0},\n  year      = {2023},\n  doi       = {10.5281/zenodo.10369245},\n  url       = {https://github.com/chiang-yuan/muse}\n}\n```\n\n## Contributing\n\nContributions are welcome! See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.\n\n## License\n\nThis project is licensed under the MIT License — see [LICENSE](LICENSE) for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchiang-yuan%2Fmuse","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchiang-yuan%2Fmuse","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchiang-yuan%2Fmuse/lists"}