{"id":"https://openalex.org/W7139130889","doi":"https://doi.org/10.48550/arxiv.2603.15712","title":"LLM-Driven Discovery of High-Entropy Catalysts via Retrieval-Augmented Generation","display_name":"LLM-Driven Discovery of High-Entropy Catalysts via Retrieval-Augmented Generation","publication_year":2026,"publication_date":"2026-03-16","ids":{"openalex":"https://openalex.org/W7139130889","doi":"https://doi.org/10.48550/arxiv.2603.15712"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.15712","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.15712","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2603.15712","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130107374","display_name":"AI Scientists","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Scientists, AI","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129795161","display_name":"Xinyi Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Xinyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129767350","display_name":"Danqing Yin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yin, Danqing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129755942","display_name":"Ying Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Ying","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":0.9567000269889832,"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":0.9567000269889832,"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/T11784","display_name":"CO2 Reduction Techniques and Catalysts","score":0.02160000056028366,"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":0.006800000090152025,"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/limiting","display_name":"Limiting","score":0.5728999972343445},{"id":"https://openalex.org/keywords/chemical-space","display_name":"Chemical space","score":0.5508000254631042},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4830999970436096},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.47699999809265137},{"id":"https://openalex.org/keywords/interpretation","display_name":"Interpretation (philosophy)","score":0.45669999718666077},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.44530001282691956},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.4011000096797943}],"concepts":[{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.5728999972343445},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5625},{"id":"https://openalex.org/C99726746","wikidata":"https://www.wikidata.org/wiki/Q906396","display_name":"Chemical space","level":3,"score":0.5508000254631042},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4830999970436096},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.47699999809265137},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.45669999718666077},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.44530001282691956},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.4011000096797943},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3677999973297119},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.36489999294281006},{"id":"https://openalex.org/C167651023","wikidata":"https://www.wikidata.org/wiki/Q1474611","display_name":"Plot (graphics)","level":2,"score":0.3643999993801117},{"id":"https://openalex.org/C183696295","wikidata":"https://www.wikidata.org/wiki/Q2487696","display_name":"Biochemical engineering","level":1,"score":0.3424000144004822},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3361000120639801},{"id":"https://openalex.org/C21880701","wikidata":"https://www.wikidata.org/wiki/Q2144042","display_name":"Process engineering","level":1,"score":0.3314000070095062},{"id":"https://openalex.org/C171250308","wikidata":"https://www.wikidata.org/wiki/Q11468","display_name":"Nanotechnology","level":1,"score":0.3165999948978424},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.3086000084877014},{"id":"https://openalex.org/C186399102","wikidata":"https://www.wikidata.org/wiki/Q903517","display_name":"Chemical stability","level":2,"score":0.28839999437332153},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.28790000081062317},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C161790260","wikidata":"https://www.wikidata.org/wiki/Q82264","display_name":"Catalysis","level":2,"score":0.2648000121116638}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.15712","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.15712","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2603.15712","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.15712","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"CO2":[0],"reduction":[1],"requires":[2],"efficient":[3],"catalysts,":[4],"yet":[5],"materials":[6,73,177],"discovery":[7,30,178],"remains":[8],"bottlenecked":[9],"by":[10,32,59],"10-20":[11],"year":[12],"development":[13],"cycles":[14],"requiring":[15],"deep":[16],"domain":[17],"expertise.":[18],"This":[19],"paper":[20],"demonstrates":[21,168],"how":[22],"large":[23],"language":[24,69,173],"models":[25],"can":[26,156,175],"assist":[27],"the":[28,135,189],"catalyst":[29,80],"process":[31],"helping":[33],"researchers":[34,181],"explore":[35,183],"chemical":[36,57,184],"spaces":[37,185],"and":[38,101,195],"interpret":[39],"results":[40],"when":[41],"augmented":[42],"with":[43,82,96],"retrieval-based":[44],"grounding.":[45],"We":[46],"introduce":[47],"a":[48,61],"retrieval-augmented":[49,154],"generation":[50,155],"framework":[51],"that":[52,128,153],"enables":[53],"GPT-4":[54],"to":[55,147,182],"navigate":[56],"space":[58],"accessing":[60],"database":[62],"of":[63,130],"50,000+":[64],"known":[65],"materials,":[66],"adapting":[67],"general-purpose":[68],"understanding":[70],"for":[71],"high-throughput":[72,149],"design.":[74],"Our":[75],"approach":[76,170],"generated":[77],"over":[78,114],"250":[79],"candidates":[81],"an":[83,169],"82%":[84],"thermodynamic":[85],"stability":[86,103],"rate":[87],"while":[88,116,139,188],"addressing":[89],"multi-objective":[90],"constraints:":[91],"68%":[92],"achieved":[93],"&lt;$100/kg":[94],"cost":[95],"metallic":[97],"conductivity":[98],"(band":[99],"gap&lt;0.1eV)":[100],"mechanical":[102],"(B/G&gt;1.75).":[104],"The":[105],"best-performing":[106],"Fe0.2Co0.2Ni0.2Ir0.1Ru0.3":[107],"achieves":[108,142],"0.285V":[109],"limiting":[110],"potential":[111],"(25%":[112],"improvement":[113],"IrO2),":[115],"Cr0.2Fe0.2Co0.3Ni0.2Mo0.1":[117],"optimally":[118],"balances":[119],"performance-cost":[120],"trade-offs":[121],"at":[122],"$18/kg.":[123],"Volcano":[124],"plot":[125],"analysis":[126],"confirms":[127],"78%":[129],"LLM-generated":[131],"catalysts":[132],"cluster":[133],"near":[134],"theoretical":[136],"activity":[137],"optimum,":[138],"our":[140],"system":[141],"200x":[143],"computational":[144],"efficiency":[145],"compared":[146],"traditional":[148],"screening.":[150],"By":[151],"demonstrating":[152],"ground":[157],"AI":[158],"creativity":[159],"in":[160,192],"physical":[161],"constraints":[162],"without":[163],"sacrificing":[164],"exploration,":[165],"this":[166],"work":[167],"where":[171],"natural":[172],"interfaces":[174],"streamline":[176],"workflows,":[179],"enabling":[180],"more":[186],"efficiently":[187],"LLM":[190],"assists":[191],"result":[193],"interpretation":[194],"hypothesis":[196],"generation.":[197]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-20T00:00:00"}
