{"id":"https://openalex.org/W7163205992","doi":"https://doi.org/10.48550/arxiv.2606.00315","title":"Coupling Language Models with Physics-based Simulation for Synthesis of Inorganic Materials","display_name":"Coupling Language Models with Physics-based Simulation for Synthesis of Inorganic Materials","publication_year":2026,"publication_date":"2026-05-29","ids":{"openalex":"https://openalex.org/W7163205992","doi":"https://doi.org/10.48550/arxiv.2606.00315"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.00315","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.00315","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.2606.00315","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5058046416","display_name":"Edward W. Staley","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Staley, Edward W.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137703959","display_name":"Tom Arbaugh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arbaugh, Tom","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110234950","display_name":"Michael Pekala","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pekala, Michael","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035926574","display_name":"Alexander New","orcid":"https://orcid.org/0000-0001-8369-1473"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"New, Alexander","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084263396","display_name":"Christopher D. Stiles","orcid":"https://orcid.org/0000-0001-6067-6051"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stiles, Christopher D.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027907644","display_name":"Nam Q. Le","orcid":"https://orcid.org/0000-0001-9266-0054"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Le, Nam Q.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083398401","display_name":"Gregory Bassen","orcid":"https://orcid.org/0009-0006-5683-7282"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bassen, Gregory","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5094275073","display_name":"Wyatt Bunstine","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bunstine, Wyatt","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135161067","display_name":"Tyrel McQueen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"McQueen, Tyrel","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":0.9968000054359436,"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":0.9968000054359436,"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/T11825","display_name":"Catalysis and Oxidation Reactions","score":0.0010000000474974513,"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"}},{"id":"https://openalex.org/T12557","display_name":"Inorganic Chemistry and Materials","score":0.00039999998989515007,"subfield":{"id":"https://openalex.org/subfields/1604","display_name":"Inorganic 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/coupling","display_name":"Coupling (piping)","score":0.5379999876022339},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.423799991607666},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4131999909877777},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.4011000096797943},{"id":"https://openalex.org/keywords/yield","display_name":"Yield (engineering)","score":0.3783000111579895},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.3467000126838684}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5845000147819519},{"id":"https://openalex.org/C131584629","wikidata":"https://www.wikidata.org/wiki/Q4308705","display_name":"Coupling (piping)","level":2,"score":0.5379999876022339},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.423799991607666},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4131999909877777},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.4011000096797943},{"id":"https://openalex.org/C134121241","wikidata":"https://www.wikidata.org/wiki/Q899301","display_name":"Yield (engineering)","level":2,"score":0.3783000111579895},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3467000126838684},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.328900009393692},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3276999890804291},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.3253999948501587},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3188000023365021},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27880001068115234},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.27810001373291016},{"id":"https://openalex.org/C179603123","wikidata":"https://www.wikidata.org/wiki/Q1941921","display_name":"Modeling language","level":3,"score":0.2750000059604645},{"id":"https://openalex.org/C2777909354","wikidata":"https://www.wikidata.org/wiki/Q1386523","display_name":"Production planning","level":3,"score":0.2614000141620636}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.00315","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.00315","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.2606.00315","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.00315","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Modern":[0],"generative":[1],"machine":[2],"learning":[3],"(ML)":[4],"models":[5,61],"can":[6,106],"propose":[7],"novel":[8,41],"inorganic":[9,51],"crystalline":[10],"materials":[11,20],"with":[12,58,84,95],"targeted":[13],"properties;":[14],"however,":[15],"synthesis":[16,52,65,93],"planning":[17,53],"of":[18,27,35,133],"these":[19],"remains":[21],"difficult":[22],"due":[23],"to":[24,44,62],"the":[25,28,74,101,130,134,139],"complexity":[26,132],"associated":[29],"physical":[30],"processes":[31],"and":[32,136],"limited":[33],"availability":[34],"computational":[36,88],"tools.":[37],"We":[38],"introduce":[39],"a":[40,68,121,125],"hybrid":[42],"framework":[43],"evaluate":[45],"Large":[46],"Language":[47],"Models":[48],"(LLMs)":[49],"in":[50,104],"by":[54],"combining":[55],"thermodynamic":[56],"databases":[57],"simplified":[59],"kinetics":[60],"approximate":[63],"realistic":[64],"conditions.":[66],"As":[67],"case":[69],"study,":[70],"we":[71,90],"focus":[72],"on":[73],"niobium-oxygen":[75],"system,":[76],"which":[77],"features":[78],"multiple":[79],"industrially":[80],"relevant":[81],"oxide":[82],"phases":[83],"well-characterized":[85],"data.":[86],"In":[87,111],"simulations,":[89],"compare":[91],"LLM-generated":[92],"routes":[94],"classical":[96,115],"path-planning":[97],"algorithms,":[98],"showing":[99],"that":[100],"implicit":[102,141],"priors":[103,142],"LLMs":[105],"yield":[107],"more":[108],"viable":[109],"strategies.":[110],"our":[112],"evaluation":[113],"setting,":[114],"search":[116],"methods":[117],"serve":[118],"primarily":[119],"as":[120],"foil":[122],"rather":[123],"than":[124],"direct":[126],"competitor.":[127],"This":[128],"illustrates":[129],"relative":[131],"problem":[135],"highlights":[137],"where":[138],"LLM's":[140],"add":[143],"value.":[144]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-03T00:00:00"}
