{"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-generative-markov-state-models","title":"Deep Generative Markov State Models","arxiv_id":"1805.07601","date":"2018-05-19","proceeding":"NeurIPS 2018 12","authors":["Hao Wu","Andreas Mardt","Luca Pasquali","Frank Noe"],"abstract":"We propose a deep generative Markov State Model (DeepGenMSM) learning\nframework for inference of metastable dynamical systems and prediction of\ntrajectories. After unsupervised training on time series data, the model\ncontains (i) a probabilistic encoder that maps from high-dimensional\nconfiguration space to a small-sized vector indicating the membership to\nmetastable (long-lived) states, (ii) a Markov chain that governs the\ntransitions between metastable states and facilitates analysis of the long-time\ndynamics, and (iii) a generative part that samples the conditional distribution\nof configurations in the next time step. The model can be operated in a\nrecursive fashion to generate trajectories to predict the system evolution from\na defined starting state and propose new configurations. The DeepGenMSM is\ndemonstrated to provide accurate estimates of the long-time kinetics and\ngenerate valid distributions for molecular dynamics (MD) benchmark systems.\nRemarkably, we show that DeepGenMSMs are able to make long time-steps in\nmolecular configuration space and generate physically realistic structures in\nregions that were not seen in training data.","url_abs":"http://arxiv.org/abs/1805.07601v2","url_pdf":"http://arxiv.org/pdf/1805.07601v2.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-generative-markov-state-models","repo_url":"https://github.com/amardt/DeepGenMSM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"deep-generative-markov-state-models","repo_url":"https://github.com/markovmodel/deep_gen_msm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":null,"task_name":"valid"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.07601","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.07601"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/amardt/DeepGenMSM","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/markovmodel/deep_gen_msm","reach":{"status":"ok"}}],"summary":{"ran_violates":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"ecf27181a9ff5ad8","entry":"potential_function","repo":"amardt/DeepGenMSM","repo_kind":"official","path":"Prinz/deep_ed_0.py","file_url":"https://github.com/amardt/DeepGenMSM/blob/HEAD/Prinz/deep_ed_0.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"ecf27181a9ff5ad8"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}