{"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/cracking-the-quantum-scaling-limit-with","title":"Cracking the Quantum Scaling Limit with Machine Learned Electron Densities","arxiv_id":"2201.03726","date":"2022-01-11","proceeding":null,"authors":["Joshua A. Rackers","Lucas Tecot","Mario Geiger","Tess E. Smidt"],"abstract":"A long-standing goal of science is to accurately solve the Schr\\\"odinger equation for large molecular systems. The poor scaling of current quantum chemistry algorithms on classical computers imposes an effective limit of about a few dozen atoms for which we can calculate molecular electronic structure. We present a machine learning (ML) method to break through this scaling limit and make quantum chemistry calculations of very large systems possible. We show that Euclidean Neural Networks can be trained to predict the electron density with high fidelity from limited data. Learning the electron density allows us to train a machine learning model on small systems and make accurate predictions on large ones. We show that this ML electron density model can break through the quantum scaling limit and calculate the electron density of systems of thousands of atoms with quantum accuracy.","url_abs":"https://arxiv.org/abs/2201.03726v2","url_pdf":"https://arxiv.org/pdf/2201.03726v2.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":"cracking-the-quantum-scaling-limit-with","repo_url":"https://github.com/joshrackers/equivariant_electron_density","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2201.03726","atlas_url":"https://app.syntology.ai/?focus=2201.03726","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.03726"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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/joshrackers/equivariant_electron_density","reach":null}],"summary":{"ran_draft_wrong":2},"by_repo_kind":{"official":{"samples":2,"ran":2,"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":2,"samples":[{"code_sha256_prefix":"a2dba53aef14d813","entry":"get_densities","repo":"joshrackers/equivariant_electron_density","repo_kind":"official","path":"generate_density_datasets/create_dataset.py","file_url":"https://github.com/joshrackers/equivariant_electron_density/blob/HEAD/generate_density_datasets/create_dataset.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":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"a2dba53aef14d813"}},{"code_sha256_prefix":"5df7156bf4220437","entry":"get_energy_force","repo":"joshrackers/equivariant_electron_density","repo_kind":"official","path":"generate_density_datasets/create_dataset.py","file_url":"https://github.com/joshrackers/equivariant_electron_density/blob/HEAD/generate_density_datasets/create_dataset.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":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"5df7156bf4220437"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}