{"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/pfp-universal-neural-network-potential-for","title":"Towards Universal Neural Network Potential for Material Discovery Applicable to Arbitrary Combination of 45 Elements","arxiv_id":"2106.14583","date":"2021-06-28","proceeding":null,"authors":["So Takamoto","Chikashi Shinagawa","Daisuke Motoki","Kosuke Nakago","Wenwen Li","Iori Kurata","Taku Watanabe","Yoshihiro Yayama","Hiroki Iriguchi","Yusuke Asano","Tasuku Onodera","Takafumi Ishii","Takao Kudo","Hideki Ono","Ryohto Sawada","Ryuichiro Ishitani","Marc Ong","Taiki Yamaguchi","Toshiki Kataoka","Akihide Hayashi","Nontawat Charoenphakdee","Takeshi Ibuka"],"abstract":"Computational material discovery is under intense study owing to its ability to explore the vast space of chemical systems. Neural network potentials (NNPs) have been shown to be particularly effective in conducting atomistic simulations for such purposes. However, existing NNPs are generally designed for narrow target materials, making them unsuitable for broader applications in material discovery. To overcome this issue, we have developed a universal NNP called PreFerred Potential (PFP), which is able to handle any combination of 45 elements. Particular emphasis is placed on the datasets, which include a diverse set of virtual structures used to attain the universality. We demonstrated the applicability of PFP in selected domains: lithium diffusion in LiFeSO${}_4$F, molecular adsorption in metal-organic frameworks, an order-disorder transition of Cu-Au alloys, and material discovery for a Fischer-Tropsch catalyst. They showcase the power of PFP, and this technology provides a highly useful tool for material discovery.","url_abs":"https://arxiv.org/abs/2106.14583v2","url_pdf":"https://arxiv.org/pdf/2106.14583v2.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":"pfp-universal-neural-network-potential-for","repo_url":"https://github.com/pfnet-research/torch-dftd","is_official":1,"mentioned_in_paper":1,"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=2106.14583","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.14583"}},"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/pfnet-research/torch-dftd","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":3},"by_repo_kind":{"official":{"samples":3,"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":"f4d0fc8b06d8e29f","entry":"calc_distances","repo":"pfnet-research/torch-dftd","repo_kind":"official","path":"torch_dftd/functions/distance.py","file_url":"https://github.com/pfnet-research/torch-dftd/blob/HEAD/torch_dftd/functions/distance.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":"f4d0fc8b06d8e29f"}},{"code_sha256_prefix":"531c999e67b3c25e","entry":"get_dftd3_default_params","repo":"pfnet-research/torch-dftd","repo_kind":"official","path":"torch_dftd/dftd3_xc_params.py","file_url":"https://github.com/pfnet-research/torch-dftd/blob/HEAD/torch_dftd/dftd3_xc_params.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":"531c999e67b3c25e"}},{"code_sha256_prefix":"525ec32654203df5","entry":"poly_smoothing","repo":"pfnet-research/torch-dftd","repo_kind":"official","path":"torch_dftd/functions/smoothing.py","file_url":"https://github.com/pfnet-research/torch-dftd/blob/HEAD/torch_dftd/functions/smoothing.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":"525ec32654203df5"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}