{"id":13477926,"url":"https://github.com/aiqm/torchani","last_synced_at":"2026-03-02T15:34:19.940Z","repository":{"id":37768188,"uuid":"127776748","full_name":"aiqm/torchani","owner":"aiqm","description":"Accurate Neural Network Potential on PyTorch","archived":false,"fork":false,"pushed_at":"2024-10-29T16:15:42.000Z","size":77738,"stargazers_count":464,"open_issues_count":24,"forks_count":128,"subscribers_count":30,"default_branch":"master","last_synced_at":"2024-11-17T14:03:44.921Z","etag":null,"topics":["deep-learning","force-field","molecular-simulation","neural-network","quantum-chemistry","quantum-mechanics"],"latest_commit_sha":null,"homepage":"https://aiqm.github.io/torchani/","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/aiqm.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","codeowners":"CODEOWNERS","security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-04-02T15:43:04.000Z","updated_at":"2024-11-02T01:07:13.000Z","dependencies_parsed_at":"2024-06-18T18:14:16.977Z","dependency_job_id":"b60b6ce5-9810-4b2c-b9ff-ac7e51973ee8","html_url":"https://github.com/aiqm/torchani","commit_stats":{"total_commits":432,"total_committers":17,"mean_commits":25.41176470588235,"dds":0.3032407407407407,"last_synced_commit":"40cf334dbdf71903f30be8560784ca793b830221"},"previous_names":[],"tags_count":26,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aiqm%2Ftorchani","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aiqm%2Ftorchani/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aiqm%2Ftorchani/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aiqm%2Ftorchani/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/aiqm","download_url":"https://codeload.github.com/aiqm/torchani/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245802154,"owners_count":20674597,"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":["deep-learning","force-field","molecular-simulation","neural-network","quantum-chemistry","quantum-mechanics"],"created_at":"2024-07-31T16:01:49.941Z","updated_at":"2025-10-21T20:55:05.094Z","avatar_url":"https://github.com/aiqm.png","language":"Python","funding_links":[],"categories":["Python","Pytorch \u0026 related libraries｜Pytorch \u0026 相关库","Software","Interatomic Potentials (ML-IAP)","Pytorch \u0026 related libraries","🔥 Reactive Chemistry"],"sub_categories":["Other libraries｜其他库:","Other libraries:","🧪 Datasets (Reactive)"],"readme":"# \u003cimg src=https://raw.githubusercontent.com/aiqm/torchani/master/logo1.png width=180/\u003e  Accurate Neural Network Potential on PyTorch\n\nMetrics:\n\n![PyPI](https://img.shields.io/pypi/v/torchani.svg)\n![PyPI - Downloads](https://img.shields.io/pypi/dm/torchani.svg)\n\nChecks:\n\n[![CodeFactor](https://www.codefactor.io/repository/github/aiqm/torchani/badge/master)](https://www.codefactor.io/repository/github/aiqm/torchani/overview/master)\n[![Total alerts](https://img.shields.io/lgtm/alerts/g/aiqm/torchani.svg?logo=lgtm\u0026logoWidth=18)](https://lgtm.com/projects/g/aiqm/torchani/alerts/)\n[![Actions Status](https://github.com/aiqm/torchani/workflows/flake8/badge.svg)](https://github.com/aiqm/torchani/actions)\n[![Actions Status](https://github.com/aiqm/torchani/workflows/clang-format/badge.svg)](https://github.com/aiqm/torchani/actions)\n[![Actions Status](https://github.com/aiqm/torchani/workflows/mypy/badge.svg)](https://github.com/aiqm/torchani/actions)\n[![Actions Status](https://github.com/aiqm/torchani/workflows/unittests/badge.svg)](https://github.com/aiqm/torchani/actions)\n[![Actions Status](https://github.com/aiqm/torchani/workflows/cuda/badge.svg)](https://github.com/aiqm/torchani/actions)\n[![Actions Status](https://github.com/aiqm/torchani/workflows/docs/badge.svg)](https://github.com/aiqm/torchani/actions)\n[![Actions Status](https://github.com/aiqm/torchani/workflows/runnable-submodules/badge.svg)](https://github.com/aiqm/torchani/actions)\n[![Actions Status](https://github.com/aiqm/torchani/workflows/tools/badge.svg)](https://github.com/aiqm/torchani/actions)\n\nDeploy:\n\n[![Actions Status](https://github.com/aiqm/torchani/workflows/deploy-docs/badge.svg)](https://github.com/aiqm/torchani/actions)\n[![Actions Status](https://github.com/aiqm/torchani/workflows/deploy-pypi/badge.svg)](https://github.com/aiqm/torchani/actions)\n\nWe only provide compatibility with nightly PyTorch, but you can check if stable PyTorch happens to be supported by looking at the following badge:\n\n[![Actions Status](https://github.com/aiqm/torchani/workflows/stable-torch/badge.svg)](https://github.com/aiqm/torchani/actions)\n\n\nTorchANI is a pytorch implementation of ANI. It is currently under alpha release, which means, the API is not stable yet. If you find a bug of TorchANI, or have some feature request, feel free to open an issue on GitHub, or send us a pull request.\n\n\u003cimg src=https://raw.githubusercontent.com/aiqm/torchani/master/logo2.png width=500/\u003e\n\n\n# Install\n\nTorchANI requires the latest preview version of PyTorch. Please install PyTorch before installing TorchANI.\n\nPlease see [PyTorch's official site](https://pytorch.org/get-started/locally/) for instructions of installing latest preview version of PyTorch.\n\nNote that if you updated TorchANI, you may also need to update PyTorch.\n\nAfter installing the correct PyTorch, you can install TorchANI by `pip` or `conda`:\n\n```bash\npip install torchani\n```\n\nor\n\n```bash\nconda install -c conda-forge torchani\n```\n\nSee https://github.com/conda-forge/torchani-feedstock for more information about the conda package.\n\nTo run the tests and examples, you must manually download a data package\n\n```bash\n./download.sh\n```\n\n[CUAEV](https://github.com/aiqm/torchani/tree/master/torchani/cuaev) (Optional)  \nTo install AEV CUDA Extension (speedup for AEV forward and backward), please follow the instruction at [torchani/cuaev](https://github.com/aiqm/torchani/tree/master/torchani/cuaev).\n\n# Citation\n\nPlease cite the following paper if you use TorchANI \n\n* Xiang Gao, Farhad Ramezanghorbani, Olexandr Isayev, Justin S. Smith, and Adrian E. Roitberg. *TorchANI: A Free and Open Source PyTorch Based Deep Learning Implementation of the ANI Neural Network Potentials*. Journal of Chemical Information and Modeling 2020 60 (7), 3408-3415, [![DOI for Citing](https://img.shields.io/badge/DOI-10.1021%2Facs.jcim.0c00451-green.svg)](https://doi.org/10.1021/acs.jcim.0c00451)\n\n[![JCIM Cover](https://pubs.acs.org/na101/home/literatum/publisher/achs/journals/content/jcisd8/2020/jcisd8.2020.60.issue-7/jcisd8.2020.60.issue-7/20200727/jcisd8.2020.60.issue-7.largecover.jpg)](https://pubs.acs.org/toc/jcisd8/60/7)\n\n* Please refer to [isayev/ASE_ANI](https://github.com/isayev/ASE_ANI) for ANI model references.\n\n# ANI model parameters\nAll the ANI model parameters including (ANI2x, ANI1x, and ANI1ccx) are accessible from the following repositories:\n- [isayev/ASE_ANI](https://github.com/isayev/ASE_ANI)\n- [aiqm/ani-model-zoo](https://github.com/aiqm/ani-model-zoo)\n\n\n# Develop\n\nTo install TorchANI from GitHub:\n\n```bash\ngit clone https://github.com/aiqm/torchani.git\ncd torchani\npip install -e .\n```\n\nAfter TorchANI has been installed, you can build the documents by running `sphinx-build docs build`. But make sure you\ninstall dependencies:\n```bash\npip install -r docs_requirements.txt\n```\n\nTo manually run unit tests, do\n\n```bash\npytest -v\n```\n\nIf you opened a pull request, you could see your generated documents at https://aiqm.github.io/torchani-test-docs/ after you `docs` check succeed.\nKeep in mind that this repository is only for the purpose of convenience of development, and only keeps the latest push.\nThe CI runing for other pull requests might overwrite this repository. You could rerun the `docs` check to overwrite this repo to your build.\n\n\n# Note to TorchANI developers\n\nNever commit to the master branch directly. If you need to change something, create a new branch, submit a PR on GitHub.\n\nYou must pass all the tests on GitHub before your PR can be merged.\n\nCode review is required before merging pull request.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faiqm%2Ftorchani","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faiqm%2Ftorchani","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faiqm%2Ftorchani/lists"}