{"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/schnet-a-deep-learning-architecture-for","title":"SchNet - a deep learning architecture for molecules and materials","arxiv_id":"1712.06113","date":"2017-12-17","proceeding":null,"authors":["Kristof T. Schütt","Huziel E. Sauceda","Pieter-Jan Kindermans","Alexandre Tkatchenko","Klaus-Robert Müller"],"abstract":"Deep learning has led to a paradigm shift in artificial intelligence, including web, text and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics. Machine learning in general and deep learning in particular is ideally suited for representing quantum-mechanical interactions, enabling to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space. Here we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for \\emph{molecules and materials} where our model learns chemically plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules and perform an exemplary study of the quantum-mechanical properties of C$_{20}$-fullerene that would have been infeasible with regular ab initio molecular dynamics.","url_abs":"http://arxiv.org/abs/1712.06113v3","url_pdf":"http://arxiv.org/pdf/1712.06113v3.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":"schnet-a-deep-learning-architecture-for","repo_url":"https://github.com/atomistic-machine-learning/schnetpack","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"schnet-a-deep-learning-architecture-for","repo_url":"https://github.com/Tony-Y/cgnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"schnet-a-deep-learning-architecture-for","repo_url":"https://github.com/dcccc/LC_NET","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"schnet-a-deep-learning-architecture-for","repo_url":"https://github.com/dcccc/git_python","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"schnet-a-deep-learning-architecture-for","repo_url":"https://github.com/peterbjorgensen/msgnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/formation-energy-on-materials-project","task":"Formation Energy","dataset":"Materials Project","model":"SchNet","rank_in_archive_order":7,"of":9,"metrics":{"MAE":"35"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1712.06113","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1712.06113"}},"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/atomistic-machine-learning/schnetpack","reach":{"status":"ok","spdx":"NOASSERTION"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Tony-Y/cgnn","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/dcccc/LC_NET","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/dcccc/git_python","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/peterbjorgensen/msgnet","reach":null}],"summary":{"ran_honours":2,"ran_draft_wrong":1,"ran_violates":1},"by_repo_kind":{"listed":{"samples":4,"ran":4,"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":"f6861553018d2738","entry":"float_or_string","repo":"peterbjorgensen/msgnet","repo_kind":"listed","path":"src/scripts/runner.py","file_url":"https://github.com/peterbjorgensen/msgnet/blob/HEAD/src/scripts/runner.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"f6861553018d2738"}},{"code_sha256_prefix":"c3c3392a3d0f7172","entry":"get_arguments","repo":"peterbjorgensen/msgnet","repo_kind":"listed","path":"src/scripts/runner.py","file_url":"https://github.com/peterbjorgensen/msgnet/blob/HEAD/src/scripts/runner.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c3c3392a3d0f7172"}},{"code_sha256_prefix":"61dc9fc0e2272217","entry":"list_of_lists","repo":"peterbjorgensen/msgnet","repo_kind":"listed","path":"src/scripts/runner.py","file_url":"https://github.com/peterbjorgensen/msgnet/blob/HEAD/src/scripts/runner.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"61dc9fc0e2272217"}},{"code_sha256_prefix":"6664fee3d8969be5","entry":"species_count_filter","repo":"peterbjorgensen/msgnet","repo_kind":"listed","path":"src/scripts/predict_with_model.py","file_url":"https://github.com/peterbjorgensen/msgnet/blob/HEAD/src/scripts/predict_with_model.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"6664fee3d8969be5"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}