{"id":13458493,"url":"https://github.com/deepmodeling/dpgen","last_synced_at":"2025-05-15T23:03:52.092Z","repository":{"id":37934216,"uuid":"191752255","full_name":"deepmodeling/dpgen","owner":"deepmodeling","description":"The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field","archived":false,"fork":false,"pushed_at":"2025-05-13T09:41:13.000Z","size":8578,"stargazers_count":337,"open_issues_count":53,"forks_count":179,"subscribers_count":10,"default_branch":"master","last_synced_at":"2025-05-13T10:41:46.173Z","etag":null,"topics":["active-learning","concurrent-learning","python"],"latest_commit_sha":null,"homepage":"https://docs.deepmodeling.com/projects/dpgen/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"lgpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/deepmodeling.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"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}},"created_at":"2019-06-13T11:43:56.000Z","updated_at":"2025-05-13T02:56:34.000Z","dependencies_parsed_at":"2023-11-28T06:23:19.453Z","dependency_job_id":"167bc990-f5f1-4353-a226-0acbecdcdc16","html_url":"https://github.com/deepmodeling/dpgen","commit_stats":{"total_commits":1469,"total_committers":67,"mean_commits":"21.925373134328357","dds":0.7488087134104833,"last_synced_commit":"4be4f6daea8373858a7da98a14f704e52e2ba4dc"},"previous_names":[],"tags_count":31,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deepmodeling%2Fdpgen","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deepmodeling%2Fdpgen/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deepmodeling%2Fdpgen/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deepmodeling%2Fdpgen/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/deepmodeling","download_url":"https://codeload.github.com/deepmodeling/dpgen/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254436944,"owners_count":22070946,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["active-learning","concurrent-learning","python"],"created_at":"2024-07-31T09:00:53.112Z","updated_at":"2025-05-15T23:03:51.725Z","avatar_url":"https://github.com/deepmodeling.png","language":"Python","readme":"\u003cpicture\u003e\u003csource media=\"(prefers-color-scheme: dark)\" srcset=\"./doc/_static/logo-dark.svg\"\u003e\u003csource media=\"(prefers-color-scheme: light)\" srcset=\"./doc/_static/logo.svg\"\u003e\u003cimg alt=\"DP-GEN logo\" src=\"./doc/_static/logo.svg\"\u003e\u003c/picture\u003e\n\n--------------------------------------------------------------------------------\n\n# DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models\n\n[![GitHub release](https://img.shields.io/github/release/deepmodeling/dpgen.svg?maxAge=86400)](https://github.com/deepmodeling/dpgen/releases/)\n[![doi:10.1016/j.cpc.2020.107206](https://img.shields.io/badge/DOI-10.1016%2Fj.cpc.2020.107206-blue)](https://doi.org/10.1016/j.cpc.2020.107206)\n[![Citations](https://citations.njzjz.win/10.1016/j.cpc.2020.107206)](https://badge.dimensions.ai/details/doi/10.1016/j.cpc.2020.107206)\n[![conda install](https://img.shields.io/conda/dn/conda-forge/dpgen?label=conda%20install)](https://anaconda.org/conda-forge/dpgen)\n[![pip install](https://img.shields.io/pypi/dm/dpgen?label=pip%20install)](https://pypi.org/project/dpgen)\n\nDP-GEN (Deep Potential GENerator) is a software written in Python, delicately designed to generate a deep learning based model of interatomic potential energy and force field. DP-GEN is dependent on [DeePMD-kit](https://github.com/deepmodeling/deepmd-kit/). With highly scalable interface with common softwares for molecular simulation, DP-GEN is capable to  automatically prepare scripts and maintain job queues on HPC machines (High Performance Cluster) and analyze results.\n\nIf you use this software in any publication, please cite:\n\nYuzhi Zhang, Haidi Wang, Weijie Chen, Jinzhe Zeng, Linfeng Zhang, Han Wang, and Weinan E, DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models, Computer Physics Communications, 2020, 253, 107206.\n\n## Highlighted features\n+ **Accurate and efficient**: DP-GEN is capable to sample more than tens of million structures and select only a few for first principles calculation. DP-GEN will finally obtain a uniformly accurate model.\n+ **User-friendly and automatic**: Users may install and run DP-GEN easily. Once successfully running, DP-GEN can dispatch and handle all jobs on HPCs, and thus there's no need for any personal effort.\n+ **Highly scalable**: With modularized code structures, users and developers can easily extend DP-GEN for their most relevant needs. DP-GEN currently supports for HPC systems ([Slurm](https://slurm.schedmd.com/), [PBS](https://www.openpbs.org/), LSF and cloud machines), Deep Potential interface with DeePMD-kit, MD interface with [LAMMPS](https://www.lammps.org/), [Gromacs](http://www.gromacs.org/), [AMBER](https://ambermd.org/), Calypso and *ab-initio* calculation interface with [VASP](https://www.vasp.at/), [PWSCF](https://www.quantum-espresso.org/), [CP2K](https://www.cp2k.org/), [SIESTA](https://departments.icmab.es/leem/siesta/), [Gaussian](https://gaussian.com/), Abacus, [PWmat](http://www.pwmat.com/), etc. We're sincerely welcome and embraced to users' contributions, with more possibilities and cases to use DP-GEN.\n\n## Download and Install\n\nDP-GEN only supports Python 3.9 and above. You can [setup a conda/pip environment](https://docs.deepmodeling.com/faq/conda.html), and then use one of the following methods to install DP-GEN:\n\n- Install via pip: `pip install dpgen`\n- Install via conda: `conda install -c conda-forge dpgen`\n- Install from source code: `git clone https://github.com/deepmodeling/dpgen \u0026\u0026 pip install ./dpgen`\n\nTo test if the installation is successful, you may execute\n\n```bash\ndpgen -h\n```\n\n## Workflows and usage\n\nDP-GEN contains the following workflows:\n\n* [`dpgen run`](https://docs.deepmodeling.com/projects/dpgen/en/latest/run/): Main process of Deep Potential Generator.\n* [Init](https://docs.deepmodeling.com/projects/dpgen/en/latest/init/): Generating initial data.\n  * `dpgen init_bulk`: Generating initial data for bulk systems.\n  * `dpgen init_surf`: Generating initial data for surface systems.\n  * `dpgen init_reaction`: Generating initial data for reactive systems.\n* [`dpgen simplify`](https://docs.deepmodeling.com/projects/dpgen/en/latest/simplify/): Reducing the amount of existing dataset.\n* [`dpgen autotest`](https://docs.deepmodeling.com/projects/dpgen/en/latest/autotest/): Autotest for Deep Potential.\n\nFor detailed usage and parameters, read [DP-GEN documentation](https://docs.deepmodeling.com/projects/dpgen/).\n\n## Tutorials and examples\n\n* [Tutorials](https://tutorials.deepmodeling.com/en/latest/Tutorials/DP-GEN/): basic tutorials for DP-GEN.\n* [Examples](examples): input files in [JSON](https://docs.python.org/3/library/json.html) format.\n* [Publications](https://blogs.deepmodeling.com/papers/dpgen/): Published research articles using DP-GEN.\n* [User guide](https://docs.deepmodeling.com/projects/dpgen/en/latest/user-guide/): frequently asked questions listed in troubleshooting.\n\n## License\nThe project dpgen is licensed under [GNU LGPLv3.0](./LICENSE).\n\n## Contributing\n\nDP-GEN is maintained by [DeepModeling's developers](https://docs.deepmodeling.com/projects/dpgen/en/latest/credits.html). Contributors are always welcome.\n","funding_links":[],"categories":["Others","Active learning"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeepmodeling%2Fdpgen","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdeepmodeling%2Fdpgen","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeepmodeling%2Fdpgen/lists"}