{"id":"https://openalex.org/W7225128206","doi":"https://doi.org/10.26434/chemrxiv.15011478/v1","title":"Radial Kernel Attention for Long-Range Modeling in Polymer Machine Learning Force Fields","display_name":"Radial Kernel Attention for Long-Range Modeling in Polymer Machine Learning Force Fields","publication_year":2026,"publication_date":"2026-11-02","ids":{"openalex":"https://openalex.org/W7225128206","doi":"https://doi.org/10.26434/chemrxiv.15011478/v1"},"language":"en","primary_location":{"id":"doi:10.26434/chemrxiv.15011478/v1","doi":"https://doi.org/10.26434/chemrxiv.15011478/v1","is_oa":true,"landing_page_url":"https://doi.org/10.26434/chemrxiv.15011478/v1","pdf_url":"https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15011478/v1","source":{"id":"https://openalex.org/S4393918830","display_name":"ChemRxiv","issn_l":"2573-2293","issn":["2573-2293"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"volume":null,"type":"preprint","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15011478/v1","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5072721351","display_name":"Wenbo Fu","orcid":"https://orcid.org/0000-0002-3552-9452","observed_orcids":["https://orcid.org/0000-0002-3552-9452"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenbo Fu","raw_affiliation_strings":["Rhinovate , Shenzhen , China"],"raw_orcid":"https://orcid.org/0000-0002-3552-9452","affiliations":[{"raw_affiliation_string":"Rhinovate , Shenzhen , China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5160382777","display_name":"Chenghui Mao","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chenghui Mao","raw_affiliation_strings":["Rhinovate , Shenzhen , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rhinovate , Shenzhen , China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5160340790","display_name":"Chengwen Hu","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chengwen Hu","raw_affiliation_strings":["Rhinovate , Shenzhen , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rhinovate , Shenzhen , China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5160344618","display_name":"Haifeng Lv","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haifeng Lv","raw_affiliation_strings":["Rhinovate , Shenzhen , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rhinovate , Shenzhen , China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5160362655","display_name":"Zhou Lv","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou Lv","raw_affiliation_strings":["Rhinovate , Shenzhen , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rhinovate , Shenzhen , China","institution_ids":[]}]}],"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":true,"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":0.9945999979972839,"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":0.9945999979972839,"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/T10002","display_name":"Advanced Chemical Physics Studies","score":0.0038999998942017555,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12044","display_name":"Advanced Physical and Chemical Molecular Interactions","score":0.0005000000237487257,"subfield":{"id":"https://openalex.org/subfields/1606","display_name":"Physical and Theoretical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/long-range-interactions","display_name":"long-range interactions","score":0.7480000257492065},{"id":"https://openalex.org/keywords/energy-and-force-prediction","display_name":"energy and force prediction","score":0.6449999809265137},{"id":"https://openalex.org/keywords/linear-computational-cost","display_name":"linear computational cost","score":0.3720000088214874},{"id":"https://openalex.org/keywords/conservative-forces","display_name":"conservative forces","score":0.37400001287460327}],"concepts":[{"id":"https://openalex.org/C146599234","wikidata":"https://www.wikidata.org/wiki/Q511093","display_name":"Centroid","level":2,"score":0.7314000129699707},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6140999794006348},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5134000182151794},{"id":"https://openalex.org/C57691317","wikidata":"https://www.wikidata.org/wiki/Q1289248","display_name":"Scalar (mathematics)","level":2,"score":0.43950000405311584},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.4309000074863434},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.40630000829696655},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3919000029563904},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.38690000772476196},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.3774000108242035},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33219999074935913},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.32850000262260437},{"id":"https://openalex.org/C80551277","wikidata":"https://www.wikidata.org/wiki/Q11210","display_name":"Coordinate system","level":2,"score":0.32850000262260437},{"id":"https://openalex.org/C10803110","wikidata":"https://www.wikidata.org/wiki/Q1341441","display_name":"Force field (fiction)","level":2,"score":0.3244999945163727},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.3066999912261963},{"id":"https://openalex.org/C119621388","wikidata":"https://www.wikidata.org/wiki/Q5377166","display_name":"Energy landscape","level":2,"score":0.301800012588501},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.2921999990940094},{"id":"https://openalex.org/C84551667","wikidata":"https://www.wikidata.org/wiki/Q155640","display_name":"Potential energy","level":2,"score":0.29089999198913574},{"id":"https://openalex.org/C2781341389","wikidata":"https://www.wikidata.org/wiki/Q4531977","display_name":"Energy system","level":3,"score":0.2833999991416931},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.2809000015258789},{"id":"https://openalex.org/C203616005","wikidata":"https://www.wikidata.org/wiki/Q620495","display_name":"Hessian matrix","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.27239999175071716},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.26339998841285706}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.26434/chemrxiv.15011478/v1","doi":"https://doi.org/10.26434/chemrxiv.15011478/v1","is_oa":true,"landing_page_url":"https://doi.org/10.26434/chemrxiv.15011478/v1","pdf_url":"https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15011478/v1","source":{"id":"https://openalex.org/S4393918830","display_name":"ChemRxiv","issn_l":"2573-2293","issn":["2573-2293"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.26434/chemrxiv.15011478/v1","doi":"https://doi.org/10.26434/chemrxiv.15011478/v1","is_oa":true,"landing_page_url":"https://doi.org/10.26434/chemrxiv.15011478/v1","pdf_url":"https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15011478/v1","source":{"id":"https://openalex.org/S4393918830","display_name":"ChemRxiv","issn_l":"2573-2293","issn":["2573-2293"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7225128206.pdf","grobid_xml":"https://content.openalex.org/works/W7225128206.grobid-xml"},"referenced_works_count":13,"referenced_works":["https://openalex.org/W2908510526","https://openalex.org/W2912807874","https://openalex.org/W3093149769","https://openalex.org/W3159653252","https://openalex.org/W4220834411","https://openalex.org/W4225405705","https://openalex.org/W4283641078","https://openalex.org/W4381713642","https://openalex.org/W4408876037","https://openalex.org/W7117744605","https://openalex.org/W7134245315","https://openalex.org/W7140303358","https://openalex.org/W7154044755"],"related_works":["https://openalex.org/W4387522931","https://openalex.org/W7127727667","https://openalex.org/W4386436455","https://openalex.org/W2889489560","https://openalex.org/W4409271209","https://openalex.org/W2883367257","https://openalex.org/W7119535402","https://openalex.org/W2489398736","https://openalex.org/W6889025113","https://openalex.org/W4415773090"],"abstract_inverted_index":{"We":[0,73,97],"introduce":[1],"Radial":[2],"Kernel":[3],"Attention":[4],"(RKA)":[5],"for":[6,69,103,118],"long-range":[7],"modeling":[8],"in":[9,66,107],"polymer":[10,21],"machine":[11],"learning":[12],"force":[13,101,143,177,200],"fields.":[14],"Local":[15],"neighbor":[16],"graphs":[17],"constrain":[18],"communication":[19,194],"across":[20],"chains":[22],"and":[23,46,53,57,83,100,121,142,147,158,175,199],"their":[24,48],"surrounding":[25],"environments,":[26],"while":[27],"explicit":[28],"all-pair":[29],"attention":[30],"incurs":[31],"quadratic":[32],"cost.":[33],"RKA":[34,138,152,189],"maps":[35],"atomic":[36],"distances":[37],"from":[38],"the":[39,75,89,114,127,161,203],"cluster":[40],"centroid":[41],"to":[42],"learned":[43],"radial":[44,94],"features":[45],"combines":[47],"overlap":[49],"with":[50,156],"content-dependent":[51],"queries":[52],"keys.":[54],"Factorized":[55],"aggregation":[56],"readout":[58],"enable":[59],"global":[60,193],"feature":[61,71],"exchange":[62],"at":[63],"linear":[64],"cost":[65],"atom":[67],"count":[68],"fixed":[70],"dimensions.":[72],"insert":[74],"invariant":[76],"scalar":[77],"update":[78],"between":[79],"EquiformerV3":[80],"local":[81],"blocks":[82],"obtain":[84],"conservative":[85],"forces":[86],"by":[87,145],"differentiating":[88],"total":[90],"energy,":[91],"including":[92],"its":[93,182],"coordinate":[95],"dependence.":[96],"evaluate":[98],"energy":[99,141,184,198],"prediction":[102,201],"finite":[104],"polymer-derived":[105,205],"clusters":[106],"OPoly26.":[108],"Our":[109],"models":[110],"are":[111],"trained":[112],"on":[113,126,160,202],"official":[115,129],"training":[116,125],"split":[117],"12":[119],"epochs":[120],"evaluated":[122,204],"directly":[123],"after":[124],"complete":[128],"validation":[130,163],"split.":[131],"A":[132],"matched":[133],"5M":[134],"comparison":[135],"shows":[136],"that":[137,196],"reduces":[139],"per-atom":[140,183],"MAEs":[144],"67.4%":[146],"36.3%,":[148],"respectively.":[149],"The":[150,165],"20M":[151,166],"model":[153,167],"is":[154,186],"compared":[155],"UMA-S-1.2":[157],"UMA-S-1.2.1":[159],"same":[162],"structures.":[164],"attains":[168],"a":[169,176,191],"total-energy":[170],"MAE":[171,178,185],"of":[172,179],"15.81":[173],"meV":[174],"2.619":[180],"meV/\u00c5;":[181],"0.1182":[187],"meV/atom.":[188],"provides":[190],"compact":[192],"pathway":[195],"improves":[197],"clusters.":[206]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2026-11-03T00:00:00"}
