{"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-coarse-grained-potentials-via-relative","title":"Deep Coarse-grained Potentials via Relative Entropy Minimization","arxiv_id":"2208.10330","date":"2022-08-22","proceeding":null,"authors":["Stephan Thaler","Maximilian Stupp","Julija Zavadlav"],"abstract":"Neural network (NN) potentials are a natural choice for coarse-grained (CG) models. Their many-body capacity allows highly accurate approximations of the potential of mean force, promising CG simulations at unprecedented accuracy. CG NN potentials trained bottom-up via force matching (FM), however, suffer from finite data effects: They rely on prior potentials for physically sound predictions outside the training data domain and the corresponding free energy surface is sensitive to errors in transition regions. The standard alternative to FM for classical potentials is relative entropy (RE) minimization, which has not yet been applied to NN potentials. In this work, we demonstrate for benchmark problems of liquid water and alanine dipeptide that RE training is more data efficient due to accessing the CG distribution during training, resulting in improved free energy surfaces and reduced sensitivity to prior potentials. In addition, RE learns to correct time integration errors, allowing larger time steps in CG molecular dynamics simulation while maintaining accuracy. Thus, our findings support the use of training objectives beyond FM as a promising direction for improving CG NN potential accuracy and reliability.","url_abs":"https://arxiv.org/abs/2208.10330v2","url_pdf":"https://arxiv.org/pdf/2208.10330v2.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"deep-coarse-grained-potentials-via-relative","repo_url":"https://github.com/tummfm/relative-entropy","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"jax","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2208.10330","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.10330"}},"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/tummfm/relative-entropy","reach":{"status":"ok","spdx":"Apache-2.0"}}],"summary":{"unverified":13},"by_repo_kind":{"official":{"samples":13,"ran":0,"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":0,"samples":[{"code_sha256_prefix":"3275ba422c2a789a","entry":"Jn","repo":"tummfm/relative-entropy","repo_kind":"official","path":"chemtrain/dimenet_basis_util.py","file_url":"https://github.com/tummfm/relative-entropy/blob/HEAD/chemtrain/dimenet_basis_util.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"3275ba422c2a789a"}},{"code_sha256_prefix":"a62e9b4c60eaba14","entry":"Jn_zeros","repo":"tummfm/relative-entropy","repo_kind":"official","path":"chemtrain/dimenet_basis_util.py","file_url":"https://github.com/tummfm/relative-entropy/blob/HEAD/chemtrain/dimenet_basis_util.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"a62e9b4c60eaba14"}},{"code_sha256_prefix":"973ef5f084c9d299","entry":"angle","repo":"tummfm/relative-entropy","repo_kind":"official","path":"chemtrain/sparse_graph.py","file_url":"https://github.com/tummfm/relative-entropy/blob/HEAD/chemtrain/sparse_graph.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"973ef5f084c9d299"}},{"code_sha256_prefix":"e2bbf3cfb16368c0","entry":"angle_triplets","repo":"tummfm/relative-entropy","repo_kind":"official","path":"chemtrain/sparse_graph.py","file_url":"https://github.com/tummfm/relative-entropy/blob/HEAD/chemtrain/sparse_graph.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"e2bbf3cfb16368c0"}},{"code_sha256_prefix":"b8c0cc579811e6f6","entry":"build_dataset","repo":"tummfm/relative-entropy","repo_kind":"official","path":"chemtrain/force_matching.py","file_url":"https://github.com/tummfm/relative-entropy/blob/HEAD/chemtrain/force_matching.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"b8c0cc579811e6f6"}},{"code_sha256_prefix":"c463d5ca2b2d5160","entry":"get_dataset","repo":"tummfm/relative-entropy","repo_kind":"official","path":"chemtrain/data_processing.py","file_url":"https://github.com/tummfm/relative-entropy/blob/HEAD/chemtrain/data_processing.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"c463d5ca2b2d5160"}},{"code_sha256_prefix":"3bc3d786ffabbb02","entry":"init_model","repo":"tummfm/relative-entropy","repo_kind":"official","path":"chemtrain/force_matching.py","file_url":"https://github.com/tummfm/relative-entropy/blob/HEAD/chemtrain/force_matching.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"3bc3d786ffabbb02"}},{"code_sha256_prefix":"923afb266cef624f","entry":"init_traj_mean_fn","repo":"tummfm/relative-entropy","repo_kind":"official","path":"chemtrain/traj_quantity.py","file_url":"https://github.com/tummfm/relative-entropy/blob/HEAD/chemtrain/traj_quantity.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"923afb266cef624f"}},{"code_sha256_prefix":"34b70676af5c11e7","entry":"model_init_apply","repo":"tummfm/relative-entropy","repo_kind":"official","path":"chemtrain/dropout.py","file_url":"https://github.com/tummfm/relative-entropy/blob/HEAD/chemtrain/dropout.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"34b70676af5c11e7"}},{"code_sha256_prefix":"d0b26670fee5be6c","entry":"next_dropout_params","repo":"tummfm/relative-entropy","repo_kind":"official","path":"chemtrain/dropout.py","file_url":"https://github.com/tummfm/relative-entropy/blob/HEAD/chemtrain/dropout.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"d0b26670fee5be6c"}},{"code_sha256_prefix":"56ed90732d1e1c65","entry":"safe_angle_mask","repo":"tummfm/relative-entropy","repo_kind":"official","path":"chemtrain/sparse_graph.py","file_url":"https://github.com/tummfm/relative-entropy/blob/HEAD/chemtrain/sparse_graph.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"56ed90732d1e1c65"}},{"code_sha256_prefix":"dad5a152d86c0b92","entry":"spherical_bessel_formulas","repo":"tummfm/relative-entropy","repo_kind":"official","path":"chemtrain/dimenet_basis_util.py","file_url":"https://github.com/tummfm/relative-entropy/blob/HEAD/chemtrain/dimenet_basis_util.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"dad5a152d86c0b92"}},{"code_sha256_prefix":"1043a41786b732bc","entry":"split_dropout_params","repo":"tummfm/relative-entropy","repo_kind":"official","path":"chemtrain/dropout.py","file_url":"https://github.com/tummfm/relative-entropy/blob/HEAD/chemtrain/dropout.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"1043a41786b732bc"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}