{"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/accelerating-crystal-structure-search-through","title":"Accelerating crystal structure search through active learning with neural networks for rapid relaxations","arxiv_id":"2408.04073","date":"2024-08-07","proceeding":null,"authors":["Stefaan S. P. Hessmann","Kristof T. Schütt","Niklas W. A. Gebauer","Michael Gastegger","Tamio Oguchi","Tomoki Yamashita"],"abstract":"Global optimization of crystal compositions is a significant yet computationally intensive method to identify stable structures within chemical space. The specific physical properties linked to a three-dimensional atomic arrangement make this an essential task in the development of new materials. We present a method that efficiently uses active learning of neural network force fields for structure relaxation, minimizing the required number of steps in the process. This is achieved by neural network force fields equipped with uncertainty estimation, which iteratively guide a pool of randomly generated candidates towards their respective local minima. Using this approach, we are able to effectively identify the most promising candidates for further evaluation using density functional theory (DFT). Our method not only reliably reduces computational costs by up to two orders of magnitude across the benchmark systems Si16 , Na8Cl8 , Ga8As8 and Al4O6 , but also excels in finding the most stable minimum for the unseen, more complex systems Si46 and Al16O24 . Moreover, we demonstrate at the example of Si16 that our method can find multiple relevant local minima while only adding minor computational effort.","url_abs":"https://arxiv.org/abs/2408.04073v1","url_pdf":"https://arxiv.org/pdf/2408.04073v1.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":"accelerating-crystal-structure-search-through","repo_url":"https://github.com/stefaanhessmann/active-csp","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2408.04073","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.04073"}},"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/stefaanhessmann/active-csp","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":4,"unverified":1},"by_repo_kind":{"official":{"samples":5,"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":"4a1248fa823ffe6b","entry":"cluster_by_representations_old","repo":"stefaanhessmann/active-csp","repo_kind":"official","path":"src/activecsp/selection/clustering.py","file_url":"https://github.com/stefaanhessmann/active-csp/blob/HEAD/src/activecsp/selection/clustering.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"4a1248fa823ffe6b"}},{"code_sha256_prefix":"e50c3447570ad8cd","entry":"find_ids_with_large_absolute_values","repo":"stefaanhessmann/active-csp","repo_kind":"official","path":"src/activecsp/selection/clustering.py","file_url":"https://github.com/stefaanhessmann/active-csp/blob/HEAD/src/activecsp/selection/clustering.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"e50c3447570ad8cd"}},{"code_sha256_prefix":"b22375f4214c44e0","entry":"labels_to_clusters","repo":"stefaanhessmann/active-csp","repo_kind":"official","path":"src/activecsp/selection/clustering.py","file_url":"https://github.com/stefaanhessmann/active-csp/blob/HEAD/src/activecsp/selection/clustering.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"b22375f4214c44e0"}},{"code_sha256_prefix":"a340140162cfe2b8","entry":"relative_uncertainty","repo":"stefaanhessmann/active-csp","repo_kind":"official","path":"src/activecsp/force_field/calculator.py","file_url":"https://github.com/stefaanhessmann/active-csp/blob/HEAD/src/activecsp/force_field/calculator.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"a340140162cfe2b8"}},{"code_sha256_prefix":"6675fa73985f2fee","entry":"check_distance","repo":"stefaanhessmann/active-csp","repo_kind":"official","path":"src/activecsp/structure_generation.py","file_url":"https://github.com/stefaanhessmann/active-csp/blob/HEAD/src/activecsp/structure_generation.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"6675fa73985f2fee"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}