{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deepmd-kit-a-deep-learning-package-for-many","title":"DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics","arxiv_id":"1712.03641","date":"2017-12-11","proceeding":null,"authors":["Han Wang","Linfeng Zhang","Jiequn Han","Weinan E"],"abstract":"Recent developments in many-body potential energy representation via deep\nlearning have brought new hopes to addressing the accuracy-versus-efficiency\ndilemma in molecular simulations. Here we describe DeePMD-kit, a package\nwritten in Python/C++ that has been designed to minimize the effort required to\nbuild deep learning based representation of potential energy and force field\nand to perform molecular dynamics. Potential applications of DeePMD-kit span\nfrom finite molecules to extended systems and from metallic systems to\nchemically bonded systems. DeePMD-kit is interfaced with TensorFlow, one of the\nmost popular deep learning frameworks, making the training process highly\nautomatic and efficient. On the other end, DeePMD-kit is interfaced with\nhigh-performance classical molecular dynamics and quantum (path-integral)\nmolecular dynamics packages, i.e., LAMMPS and the i-PI, respectively. Thus,\nupon training, the potential energy and force field models can be used to\nperform efficient molecular simulations for different purposes. As an example\nof the many potential applications of the package, we use DeePMD-kit to learn\nthe interatomic potential energy and forces of a water model using data\nobtained from density functional theory. We demonstrate that the resulted\nmolecular dynamics model reproduces accurately the structural information\ncontained in the original model.","url_abs":"http://arxiv.org/abs/1712.03641v2","url_pdf":"http://arxiv.org/pdf/1712.03641v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deepmd-kit-a-deep-learning-package-for-many","repo_url":"https://github.com/deepmodeling/deepmd-kit","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"LGPL-3.0"}},{"paper_slug":"deepmd-kit-a-deep-learning-package-for-many","repo_url":"https://github.com/mindspore-ai/models/tree/master/research/hpc/molecular_dynamics","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1712.03641","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}