{"id":"https://openalex.org/W7165691278","doi":"https://doi.org/10.48550/arxiv.2606.20756","title":"A large-scale foundation model enables simulation-to-real adaptation for nuclear magnetic resonance-based molecular structure analysis","display_name":"A large-scale foundation model enables simulation-to-real adaptation for nuclear magnetic resonance-based molecular structure analysis","publication_year":2026,"publication_date":"2026-06-18","ids":{"openalex":"https://openalex.org/W7165691278","doi":"https://doi.org/10.48550/arxiv.2606.20756"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.20756","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.20756","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.20756","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139145987","display_name":"Chen Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139167154","display_name":"Zheng Fang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Zheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114112994","display_name":"Hanyu Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Hanyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113394288","display_name":"Fanjie Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Fanjie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139156081","display_name":"Hongxin Xiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiang, Hongxin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076692827","display_name":"He Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Hanyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139195475","display_name":"Xiangxiang Zeng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zeng, Xiangxiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139141518","display_name":"Yuqiang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yuqiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139137531","display_name":"Xiaojian Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xiaojian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139183079","display_name":"Jun Xia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xia, Jun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.3183000087738037,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.3183000087738037,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12748","display_name":"Molecular spectroscopy and chirality","score":0.2004999965429306,"subfield":{"id":"https://openalex.org/subfields/1607","display_name":"Spectroscopy"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.15320000052452087,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/nmr-spectra-database","display_name":"NMR spectra database","score":0.6273999810218811},{"id":"https://openalex.org/keywords/nuclear-magnetic-resonance-spectroscopy","display_name":"Nuclear magnetic resonance spectroscopy","score":0.5914000272750854},{"id":"https://openalex.org/keywords/two-dimensional-nuclear-magnetic-resonance-spectroscopy","display_name":"Two-dimensional nuclear magnetic resonance spectroscopy","score":0.5196999907493591},{"id":"https://openalex.org/keywords/spectral-line","display_name":"Spectral line","score":0.4986000061035156},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.4237000048160553},{"id":"https://openalex.org/keywords/molecular-dynamics","display_name":"Molecular dynamics","score":0.4092999994754791},{"id":"https://openalex.org/keywords/spectroscopy","display_name":"Spectroscopy","score":0.32690000534057617}],"concepts":[{"id":"https://openalex.org/C67787023","wikidata":"https://www.wikidata.org/wiki/Q6954755","display_name":"NMR spectra database","level":3,"score":0.6273999810218811},{"id":"https://openalex.org/C66974803","wikidata":"https://www.wikidata.org/wiki/Q10359898","display_name":"Nuclear magnetic resonance spectroscopy","level":2,"score":0.5914000272750854},{"id":"https://openalex.org/C19549132","wikidata":"https://www.wikidata.org/wiki/Q11141463","display_name":"Two-dimensional nuclear magnetic resonance spectroscopy","level":2,"score":0.5196999907493591},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.504800021648407},{"id":"https://openalex.org/C4839761","wikidata":"https://www.wikidata.org/wiki/Q212111","display_name":"Spectral line","level":2,"score":0.4986000061035156},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.4237000048160553},{"id":"https://openalex.org/C59593255","wikidata":"https://www.wikidata.org/wiki/Q901663","display_name":"Molecular dynamics","level":2,"score":0.4092999994754791},{"id":"https://openalex.org/C186060115","wikidata":"https://www.wikidata.org/wiki/Q30336093","display_name":"Biological system","level":1,"score":0.36169999837875366},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3443000018596649},{"id":"https://openalex.org/C32891209","wikidata":"https://www.wikidata.org/wiki/Q483666","display_name":"Spectroscopy","level":2,"score":0.32690000534057617},{"id":"https://openalex.org/C163111631","wikidata":"https://www.wikidata.org/wiki/Q1952454","display_name":"Carbon-13 NMR","level":2,"score":0.3197000026702881},{"id":"https://openalex.org/C46141821","wikidata":"https://www.wikidata.org/wiki/Q209402","display_name":"Nuclear magnetic resonance","level":1,"score":0.3181999921798706},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.30160000920295715},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.2948000133037567},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.29179999232292175},{"id":"https://openalex.org/C32909587","wikidata":"https://www.wikidata.org/wiki/Q11369","display_name":"Molecule","level":2,"score":0.28139999508857727},{"id":"https://openalex.org/C103319777","wikidata":"https://www.wikidata.org/wiki/Q1979695","display_name":"Proton NMR","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.2648000121116638},{"id":"https://openalex.org/C179303850","wikidata":"https://www.wikidata.org/wiki/Q4300362","display_name":"Molecular recognition","level":3,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.20756","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.20756","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.20756","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.20756","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Nuclear":[0],"Magnetic":[1],"Resonance":[2],"(NMR)":[3],"spectroscopy":[4],"is":[5],"a":[6,52,107,138],"powerful":[7],"tool":[8],"for":[9,20,56],"molecular":[10,110,201],"structure":[11,111,202],"analysis,":[12],"and":[13,23,78,94,122,156,199],"spectral":[14,69,141],"artificial":[15],"intelligence":[16],"offers":[17],"great":[18],"potential":[19],"its":[21],"rapid":[22],"automated":[24],"interpretation.":[25],"However,":[26],"the":[27,60,166,181,194],"scarcity":[28],"of":[29,63,109,169,204],"experimental":[30,115],"NMR":[31,57,64,80,116,140,147,206],"datasets":[32],"has":[33],"constrained":[34],"deep":[35],"learning":[36],"in":[37,180],"this":[38],"domain":[39],"to":[40,66,82,106],"narrow,":[41],"task-specific":[42],"applications":[43],"that":[44,58,103,187],"lack":[45],"broad":[46],"generalization.":[47],"Here,":[48],"we":[49],"introduce":[50],"UltraNMR,":[51,84,150],"large-scale":[53,139,188],"foundation":[54],"model":[55],"leverages":[59],"intrinsic":[61],"properties":[62],"spectra":[65,81,117,148],"learn":[67],"generalizable":[68,200],"representations.":[70],"We":[71,101,135],"collected":[72],"158":[73],"million":[74,153],"paired":[75],"simulated":[76,146],"$^{1}$H":[77],"$^{13}$C":[79],"train":[83],"employing":[85],"multiple":[86],"domain-specific":[87],"pre-training":[88,190],"objectives.":[89],"UltraNMR":[90,105,125,164],"captures":[91],"both":[92],"intra-spectral":[93],"inter-spectral":[95],"dependencies,":[96],"enabling":[97,157,197],"seamless":[98],"simulation-to-real":[99,195],"adaptation.":[100],"demonstrate":[102],"adapting":[104],"range":[108],"analysis":[112,203],"tasks":[113],"on":[114,129],"consistently":[118],"yields":[119],"state-of-the-art":[120],"performance":[121],"clearly":[123],"outperforms":[124],"variants":[126],"trained":[127],"directly":[128],"downstream":[130],"data":[131],"without":[132],"simulation":[133,189],"pre-training.":[134],"also":[136],"construct":[137],"vector":[142],"library":[143],"by":[144],"encoding":[145],"using":[149],"covering":[151],"94":[152],"unique":[154],"molecules":[155],"effective":[158],"structure-aware":[159],"retrieval.":[160],"In":[161],"real-world":[162,205],"applications,":[163],"facilitates":[165],"structural":[167],"elucidation":[168],"two":[170],"previously":[171],"unknown":[172],"natural":[173],"products":[174],"from":[175],"Chinese":[176,182],"herbal":[177],"medicines":[178],"recorded":[179],"Pharmacopoeia.":[183],"These":[184],"results":[185],"suggest":[186],"can":[191],"effectively":[192],"bridge":[193],"gap,":[196],"robust":[198],"spectra.":[207]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-24T00:00:00"}