{"id":13458595,"url":"https://github.com/deepmodeling/deepks-kit","last_synced_at":"2025-08-18T20:34:53.875Z","repository":{"id":37981355,"uuid":"283388329","full_name":"deepmodeling/deepks-kit","owner":"deepmodeling","description":"a  package for developing machine learning-based chemically accurate energy and density functional models","archived":false,"fork":false,"pushed_at":"2024-09-28T05:44:36.000Z","size":7009,"stargazers_count":106,"open_issues_count":11,"forks_count":39,"subscribers_count":13,"default_branch":"master","last_synced_at":"2025-03-29T17:09:17.878Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","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":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-07-29T03:27:50.000Z","updated_at":"2025-03-24T03:03:26.000Z","dependencies_parsed_at":"2025-03-14T18:12:16.594Z","dependency_job_id":"2d6c3083-1433-4e04-b477-56df32afa1df","html_url":"https://github.com/deepmodeling/deepks-kit","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deepmodeling%2Fdeepks-kit","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deepmodeling%2Fdeepks-kit/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deepmodeling%2Fdeepks-kit/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deepmodeling%2Fdeepks-kit/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/deepmodeling","download_url":"https://codeload.github.com/deepmodeling/deepks-kit/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250285696,"owners_count":21405297,"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":[],"created_at":"2024-07-31T09:00:54.305Z","updated_at":"2025-04-22T17:10:05.072Z","avatar_url":"https://github.com/deepmodeling.png","language":"Python","readme":"# DeePKS-kit\n\nDeePKS-kit is a program to generate accurate energy functionals for quantum chemistry systems,\nfor both perturbative scheme (DeePHF) and self-consistent scheme (DeePKS).\n\nThe program provides a command line interface `deepks` that contains five sub-commands, \n- `train`: train an neural network based post-HF energy functional model\n- `test`: test the post-HF model with given data and show statistics\n- `scf`: run self-consistent field calculation with given energy model\n- `stats`: collect and print statistics of the SCF the results\n- `iterate`: iteratively train an self-consistent model by combining four commands above\n\n## Installation\n\nDeePKS-kit is a pure python library so it can be installed following the standard `git clone` then `pip install` procedure. Note that the two main requirements `pytorch` and `pyscf` will not be installed automatically so you will need to install them manually in advance. Below is a more detailed instruction that includes installing the required libraries in the environment.\n\nWe use `conda` here as an example. So first you may need to install [Anaconda](https://docs.anaconda.com/anaconda/install/) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html).\n\nTo reduce the possibility of library conflicts, we suggest create a new environment (named `deepks`) with basic dependencies installed (optional):\n```bash\nconda create -n deepks numpy scipy h5py ruamel.yaml paramiko\nconda activate deepks\n```\nNow you are in the new environment called `deepks`.\nNext, install [PyTorch](https://pytorch.org/get-started/locally/) \n```bash\n# assuming a GPU with cudatoolkit 10.2 support\nconda install pytorch cudatoolkit=10.2 -c pytorch\n```\nand [PySCF](https://github.com/pyscf/pyscf).\n```bash\n# the conda package does not support python \u003e= 3.8 so we use pip\npip install pyscf\n```\n\nOnce the environment has been setup properly, using pip to install DeePKS-kit:\n```bash\npip install git+https://github.com/deepmodeling/deepks-kit/\n```\n\n## Usage\n\nAn relatively detailed decrisption of the `deepks-kit` library can be found in [here](https://arxiv.org/pdf/2012.14615.pdf). Please also refer to the reference for the description of methods.\n\nPlease see [`examples`](./examples) folder for the usage of `deepks-kit` library. A detailed example with executable data for single water molecules can be found [here](./examples/water_single). A more complicated one for training water clusters can be found [here](./examples/water_cluster).\n\nCheck [this input file](./examples/water_cluster/args.yaml) for detailed explanation for possible input parameters, and also [this one](./examples/water_cluster/shell.yaml) if you would like to run on local machine instead of using Slurm scheduler.\n\n## References\n\n[1] Chen, Y., Zhang, L., Wang, H. and E, W., 2020. Ground State Energy Functional with Hartree–Fock Efficiency and Chemical Accuracy. The Journal of Physical Chemistry A, 124(35), pp.7155-7165.\n\n[2] Chen, Y., Zhang, L., Wang, H. and E, W., 2021. DeePKS: A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory. Journal of Chemical Theory and Computation, 17(1), pp.170–181.\n\n\n\u003c!-- ## TODO\n\n- [ ] Print loss separately for E and F in training.\n- [ ] Rewrite all `print` function using `logging`.\n- [ ] Write a detailed README and more docs.\n- [ ] Add unit tests. --\u003e\n\n","funding_links":[],"categories":["其他_生物医药","Density functional theory (ML-DFT)","Others"],"sub_categories":["网络服务_其他"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeepmodeling%2Fdeepks-kit","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdeepmodeling%2Fdeepks-kit","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeepmodeling%2Fdeepks-kit/lists"}