{"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/deep-potential-molecular-dynamics-a-scalable","title":"Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics","arxiv_id":"1707.09571","date":"2017-07-30","proceeding":null,"authors":["Linfeng Zhang","Jiequn Han","Han Wang","Roberto Car","Weinan E"],"abstract":"We introduce a scheme for molecular simulations, the Deep Potential Molecular\nDynamics (DeePMD) method, based on a many-body potential and interatomic forces\ngenerated by a carefully crafted deep neural network trained with ab initio\ndata. The neural network model preserves all the natural symmetries in the\nproblem. It is \"first principle-based\" in the sense that there are no ad hoc\ncomponents aside from the network model. We show that the proposed scheme\nprovides an efficient and accurate protocol in a variety of systems, including\nbulk materials and molecules. In all these cases, DeePMD gives results that are\nessentially indistinguishable from the original data, at a cost that scales\nlinearly with system size.","url_abs":"http://arxiv.org/abs/1707.09571v2","url_pdf":"http://arxiv.org/pdf/1707.09571v2.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":"deep-potential-molecular-dynamics-a-scalable","repo_url":"https://github.com/google/differentiable-atomistic-potentials","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"deep-potential-molecular-dynamics-a-scalable","repo_url":"https://github.com/yangyucheng000/molecular_dynamics","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mindspore","reach":{"status":"unanswered"}},{"paper_slug":"deep-potential-molecular-dynamics-a-scalable","repo_url":"https://github.com/2023-MindSpore-1/ms-code-218/tree/main/molecular_dynamics","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"deep-potential-molecular-dynamics-a-scalable","repo_url":"https://github.com/code-implementation1/Code9/tree/main/molecular_dynamics","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"deep-potential-molecular-dynamics-a-scalable","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":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1707.09571","atlas_url":"https://app.syntology.ai/?focus=1707.09571","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}