{"id":"https://openalex.org/W7165052850","doi":"https://doi.org/10.48550/arxiv.2606.17445","title":"Toward Controllable Catalyst Inverse Design via Large-Scale Autoregressive Pretraining","display_name":"Toward Controllable Catalyst Inverse Design via Large-Scale Autoregressive Pretraining","publication_year":2026,"publication_date":"2026-06-16","ids":{"openalex":"https://openalex.org/W7165052850","doi":"https://doi.org/10.48550/arxiv.2606.17445"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.17445","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.17445","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2606.17445","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001151989","display_name":"Dong Hyeon Mok","orcid":"https://orcid.org/0000-0001-5319-7052"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mok, Dong Hyeon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018459520","display_name":"Jonggeol Na","orcid":"https://orcid.org/0000-0002-1106-9500"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Na, Jonggeol","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5058710447","display_name":"Seoin Back","orcid":"https://orcid.org/0000-0003-4682-0621"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Back, Seoin","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":0.9772999882698059,"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":0.9772999882698059,"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":0.004900000058114529,"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/T11825","display_name":"Catalysis and Oxidation Reactions","score":0.003100000089034438,"subfield":{"id":"https://openalex.org/subfields/1503","display_name":"Catalysis"},"field":{"id":"https://openalex.org/fields/15","display_name":"Chemical Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.7508999705314636},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6995000243186951},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.6617000102996826},{"id":"https://openalex.org/keywords/inverse","display_name":"Inverse","score":0.5855000019073486},{"id":"https://openalex.org/keywords/catalysis","display_name":"Catalysis","score":0.4634000062942505},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4357999861240387},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.3248000144958496}],"concepts":[{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.7508999705314636},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6995000243186951},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.6617000102996826},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.5855000019073486},{"id":"https://openalex.org/C161790260","wikidata":"https://www.wikidata.org/wiki/Q82264","display_name":"Catalysis","level":2,"score":0.4634000062942505},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4357999861240387},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.42590001225471497},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3248000144958496},{"id":"https://openalex.org/C194657046","wikidata":"https://www.wikidata.org/wiki/Q7394685","display_name":"STAR model","level":4,"score":0.32190001010894775},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.320499986410141},{"id":"https://openalex.org/C142259097","wikidata":"https://www.wikidata.org/wiki/Q5891314","display_name":"Homogeneity (statistics)","level":2,"score":0.3091999888420105},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.30070000886917114},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.29190000891685486},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2750000059604645},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2750000059604645},{"id":"https://openalex.org/C1893757","wikidata":"https://www.wikidata.org/wiki/Q3653001","display_name":"Inversion (geology)","level":3,"score":0.26919999718666077},{"id":"https://openalex.org/C99726746","wikidata":"https://www.wikidata.org/wiki/Q906396","display_name":"Chemical space","level":3,"score":0.2689000070095062},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.2667999863624573}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.17445","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.17445","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.17445","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.17445","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.8175460696220398}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Inverse":[0],"design":[1],"of":[2,54,89,165],"heterogeneous":[3],"catalysts":[4,61,225],"remains":[5],"challenging":[6],"because":[7],"catalyst":[8,40,70,90,111,198,221],"surfaces":[9],"exhibit":[10],"substantial":[11],"structural":[12,136],"complexity":[13],"with":[14,62,80,121,146,211],"coupled":[15],"surface-adsorbate":[16],"interactions":[17],"across":[18],"a":[19,68,81,100,147,169,188,216],"vast":[20],"chemical":[21],"space":[22,49],"that":[23,57,85,206],"is":[24],"difficult":[25],"to":[26,190],"explore":[27],"efficiently":[28],"through":[29],"conventional":[30],"screening":[31,37,194],"alone.":[32],"Although":[33],"machine":[34],"learning-based":[35],"high-throughput":[36],"has":[38],"accelerated":[39,224],"discovery,":[41],"its":[42],"efficiency":[43,195],"inevitably":[44],"declines":[45],"as":[46],"the":[47,52,75,87,162,173,178,184],"search":[48],"grows,":[50],"motivating":[51],"development":[53],"generative":[55,71],"models":[56],"can":[58],"directly":[59],"construct":[60],"target":[63,185],"properties.":[64],"Here,":[65],"we":[66],"present":[67],"conditional":[69,129],"model":[72,105,133],"based":[73],"on":[74,93,108,116],"Generative":[76],"Pretrained":[77],"Transformer":[78],"architecture":[79],"numerical":[82],"embedding":[83],"layer":[84],"enables":[86],"generation":[88,222],"structures":[91,112,120],"conditioned":[92],"both":[94],"categorical":[95,123,143],"and":[96,113,125,141,156,177,223],"continuous":[97],"properties":[98,124],"within":[99],"single":[101],"autoregressive":[102,208],"framework.":[103],"The":[104,131],"was":[106],"pretrained":[107],"133":[109],"million":[110],"subsequently":[114],"fine-tuned":[115],"approximately":[117,166],"460,000":[118],"optimized":[119],"associated":[122],"binding":[126,159],"energies":[127],"for":[128,153,196],"generation.":[130],"resulting":[132],"achieved":[134],"98%":[135],"validity,":[137,140],"95%":[138],"optimization":[139],"high":[142],"condition":[144],"fidelity,":[145],"93":[148],"%":[149],"joint":[150],"match":[151,163],"rate":[152,164],"adsorbate":[154],"type":[155],"composition.":[157],"For":[158],"energy":[160],"conditioning,":[161,214],"20%":[167],"represents":[168],"four-fold":[170],"improvement":[171,192],"over":[172],"baseline":[174],"training":[175],"distribution,":[176],"generated":[179],"distributions":[180],"shift":[181],"systematically":[182],"toward":[183,219],"values,":[186],"enabling":[187],"1.5":[189],"4-fold":[191],"in":[193],"reaction-targeted":[197],"discovery":[199],"without":[200],"additional":[201],"fine-tuning.":[202],"These":[203],"results":[204],"show":[205],"large-scale":[207],"pre-training,":[209],"combined":[210],"explicit":[212],"property":[213],"provides":[215],"practical":[217],"route":[218],"controllable":[220],"discovery.":[226]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-18T00:00:00"}
