{"id":"https://openalex.org/W7160540924","doi":"https://doi.org/10.48550/arxiv.2605.04102","title":"Meta-LegNet: A Transferable and Interpretable Framework for Surface Adsorption Prediction via Self-Defined Adsorption-Environment Learning","display_name":"Meta-LegNet: A Transferable and Interpretable Framework for Surface Adsorption Prediction via Self-Defined Adsorption-Environment Learning","publication_year":2026,"publication_date":"2026-05-03","ids":{"openalex":"https://openalex.org/W7160540924","doi":"https://doi.org/10.48550/arxiv.2605.04102"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.04102","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04102","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.2605.04102","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135574145","display_name":"Yifan Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5105456086","display_name":"Arravind Subramanian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Subramanian, Arravind","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135603349","display_name":"Xiaoqing Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Xiaoqing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102639348","display_name":"Qiujie Lyu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lyu, Qiujie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135637397","display_name":"Sergey Kozlov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kozlov, Sergey","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135569970","display_name":"Lei Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Lei","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":0.9517999887466431,"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":0.9517999887466431,"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/T10030","display_name":"Electrocatalysts for Energy Conversion","score":0.016899999231100082,"subfield":{"id":"https://openalex.org/subfields/2105","display_name":"Renewable Energy, Sustainability and the Environment"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11009","display_name":"Catalysis for Biomass Conversion","score":0.004100000020116568,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/adsorption","display_name":"Adsorption","score":0.6079000234603882},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.5016999840736389},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.47350001335144043},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4117000102996826},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.3946000039577484},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.3824000060558319},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.376800000667572}],"concepts":[{"id":"https://openalex.org/C150394285","wikidata":"https://www.wikidata.org/wiki/Q180254","display_name":"Adsorption","level":2,"score":0.6079000234603882},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5637999773025513},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.5016999840736389},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48989999294281006},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.47350001335144043},{"id":"https://openalex.org/C186060115","wikidata":"https://www.wikidata.org/wiki/Q30336093","display_name":"Biological system","level":1,"score":0.4138999879360199},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4117000102996826},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.3946000039577484},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.3824000060558319},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.38089999556541443},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.376800000667572},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.3456999957561493},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3411000072956085},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.3240000009536743},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.3181000053882599},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.30320000648498535},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.298799991607666},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C205606062","wikidata":"https://www.wikidata.org/wiki/Q5249645","display_name":"Decoupling (probability)","level":2,"score":0.267300009727478},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2551000118255615},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2533999979496002}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.04102","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04102","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.2605.04102","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04102","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":{"A":[0],"central":[1],"challenge":[2],"in":[3,150],"computational":[4],"catalysis":[5],"is":[6],"the":[7,156],"identification":[8],"of":[9,90],"low-energy":[10],"and":[11,24,54,83,114,118,130,165,196],"chemically":[12],"plausible":[13],"adsorption":[14,20,34,92,173,190],"configurations,":[15],"as":[16],"these":[17],"directly":[18],"affect":[19],"energies,":[21],"reaction":[22],"pathways,":[23],"catalytic":[25],"performance.":[26],"Existing":[27],"approaches":[28],"generally":[29],"rely":[30],"on":[31,155,175],"enumerating":[32],"candidate":[33],"sites":[35,174],"followed":[36],"by":[37],"iterative":[38],"refinement":[39],"through":[40,124],"density":[41],"functional":[42],"theory":[43],"calculations":[44],"or":[45,62],"machine-learning-based":[46],"relaxations.":[47],"However,":[48],"such":[49],"workflows":[50],"remain":[51],"computationally":[52],"expensive":[53],"are":[55,143],"difficult":[56],"to":[57,59,86,145,170],"scale":[58],"complex":[60],"surfaces":[61,178],"multi-adsorbate":[63],"systems.":[64,97],"Here,":[65],"we":[66,159],"introduce":[67],"Meta-LegNet,":[68],"a":[69,101,167],"graph":[70],"learning":[71,188],"framework":[72],"that":[73,187],"combines":[74],"SE(3)-equivariant":[75],"atom-level":[76],"message":[77],"passing":[78],"with":[79],"voxel-based":[80],"multiscale":[81],"aggregation":[82],"cross-domain":[84],"meta-learning":[85],"learn":[87],"transferable":[88,189],"representations":[89],"local":[91,107,148],"environments":[93,109,149,191],"across":[94],"diverse":[95],"catalyst--adsorbate":[96],"Rather":[98],"than":[99],"following":[100],"conventional":[102],"regression-only":[103],"paradigm,":[104],"Meta-LegNet":[105],"encodes":[106],"chemical":[108],"using":[110],"invariant":[111],"radial":[112],"features":[113],"equivariant":[115],"directional":[116],"information,":[117],"further":[119,160],"incorporates":[120],"broader":[121],"structural":[122],"context":[123],"coordinate-frame":[125],"voxel":[126],"pooling,":[127],"assignment-based":[128],"upsampling,":[129],"gated":[131],"feature":[132],"fusion.":[133],"The":[134],"resulting":[135],"local-global":[136],"decomposition":[137],"produces":[138],"atom-resolved":[139],"attribution":[140],"maps,":[141],"which":[142],"processed":[144],"identify":[146],"adsorption-relevant":[147],"an":[151,162,193],"interpretable":[152],"manner.":[153],"Based":[154],"learned":[157],"representations,":[158],"construct":[161],"adsorption-environment":[163],"database":[164],"develop":[166],"template-matching":[168],"strategy":[169],"propose":[171],"likely":[172],"previously":[176],"unexplored":[177],"without":[179],"exhaustive":[180],"site":[181],"enumeration.":[182],"Overall,":[183],"our":[184],"results":[185],"suggest":[186],"provides":[192],"accurate,":[194],"interpretable,":[195],"practical":[197],"route":[198],"for":[199],"accelerating":[200],"catalyst":[201],"screening.":[202]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-08T00:00:00"}
