{"id":22668215,"url":"https://github.com/lanl/pyseqm","last_synced_at":"2025-04-09T22:19:31.390Z","repository":{"id":64818578,"uuid":"272572844","full_name":"lanl/PYSEQM","owner":"lanl","description":"an interface to semi-empirical quantum chemistry methods implemented with pytorch","archived":false,"fork":false,"pushed_at":"2025-04-03T21:52:28.000Z","size":809377,"stargazers_count":49,"open_issues_count":2,"forks_count":20,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-04-09T22:18:53.206Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/lanl.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":"2020-06-16T00:32:15.000Z","updated_at":"2025-04-03T21:52:32.000Z","dependencies_parsed_at":"2023-11-23T01:41:45.448Z","dependency_job_id":"fcbc48df-fbd7-4019-affd-83c0379355e9","html_url":"https://github.com/lanl/PYSEQM","commit_stats":{"total_commits":111,"total_committers":6,"mean_commits":18.5,"dds":0.3063063063063063,"last_synced_commit":"029b2989a2414468e70541eda6863975a72a5afb"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lanl%2FPYSEQM","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lanl%2FPYSEQM/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lanl%2FPYSEQM/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lanl%2FPYSEQM/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/lanl","download_url":"https://codeload.github.com/lanl/PYSEQM/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248119499,"owners_count":21050779,"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":"2024-12-09T15:14:08.649Z","updated_at":"2025-04-09T22:19:26.378Z","avatar_url":"https://github.com/lanl.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# [PYSEQM: PYtorch-based Semi-Empirical Quantum Mechanics](https://github.com/lanl/PYSEQM)\n\n[PYSEQM](https://github.com/lanl/PYSEQM) is a Semi-Empirical Quantum Mechanics package implemented in [PyTorch](http://pytorch.org). It provides built-in interfaces for machine learning and efficient molecular dynamic engines with GPU supported. Several molecular dynamics algorithms are implemented for facilitating dynamic simulations, inlcuding orginal and Extended Lagrangian Born-Oppenheimer Molecular Dynamics, geometric optimization and  several thermostats. \n\n\u003chr/\u003e\n\n## Features:\n\n* Interface with machine learning (ML) framework like [HIPNN](https://aip.scitation.org/doi/abs/10.1063/1.5011181) for ML applications and development.\n* GPU-supported Molecular Dynamics Engine\n* Stable and Efficient Extended Lagrangian Born Oppenheimer Molecular Dynamics ([XL-BOMD](https://aip.scitation.org/doi/full/10.1063/1.3148075))\n* Efficient expansion algorithm [SP2](https://journals.aps.org/prb/abstract/10.1103/PhysRevB.66.155115) for generating density matrix\n\n\n## Installation:\n\n```bash\ngit clone https://github.com/lanl/PYSEQM.git\ncd PYSEQM\npython setup.py install\n```\nor\n```bash\npip install git+https://github.com/lanl/PYSEQM.git\n```\n\nTo enable GPU with CUDA, please refer to the Installation Guide on [PyTorch website](https://pytorch.org/)\n\n## Prerequisites:\n* PyTorch\u003e=1.2\n\n## Usage:\nsee [```./doc/documentation.md```](./doc/documentation.md)\n\n## Trained model from \"Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics\"\nexamples/model/model.pt\n\n## Semi-Empirical Methods Implemented:\n1. MNDO\n2. AM1\n3. PM3\n\n\u003chr/\u003e\n\n## Authors:\n\n[Guoqing Zhou](mailto:guoqingz@usc.edu), [Benjamin Nebgen](mailto:bnebgen@lanl.gov), Nicholas Lubbers, Walter Malone, Anders M. N. Niklasson and Sergei Tretiak\n\n## Citation:\n[Zhou, Guoqing, et al. \"Graphics processing unit-accelerated semiempirical Born Oppenheimer molecular dynamics using PyTorch.\" *Journal of Chemical Theory and Computation* 16.8 (2020): 4951-4962.](https://pubs.acs.org/doi/full/10.1021/acs.jctc.0c00243)\n[Zhou, Guoqing, et al. \"Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics.\" *Proceedings of the National Academy of Sciences* 119.27 (2022): e2120333119.](https://www.pnas.org/doi/10.1073/pnas.2120333119)\n\n## Acknowledgments:\nLos Alamos National Lab (LANL), Center for Nonlinear Studies (CNLS), T-1\n\n## Copyright Notice:\n\n© (or copyright) 2020. Triad National Security, LLC. All rights reserved.\nThis program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos\nNational Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S.\nDepartment of Energy/National Nuclear Security Administration. All rights in the program are\nreserved by Triad National Security, LLC, and the U.S. Department of Energy/National Nuclear\nSecurity Administration. The Government is granted for itself and others acting on its behalf a\nnonexclusive, paid-up, irrevocable worldwide license in this material to reproduce, prepare\nderivative works, distribute copies to the public, perform publicly and display publicly, and to permit\nothers to do so.\n\n## License:\n\nThis program is open source under the BSD-3 License.\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n1. Redistributions of source code must retain the above copyright notice, this\n   list of conditions and the following disclaimer.\n\n2. Redistributions in binary form must reproduce the above copyright notice,\n   this list of conditions and the following disclaimer in the documentation\n   and/or other materials provided with the distribution.\n\n3. Neither the name of the copyright holder nor the names of its\n   contributors may be used to endorse or promote products derived from\n   this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flanl%2Fpyseqm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flanl%2Fpyseqm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flanl%2Fpyseqm/lists"}