{"id":"https://openalex.org/W7136888862","doi":"https://doi.org/10.48550/arxiv.2603.12567","title":"Foundation-Model Surrogates Enable Data-Efficient Active Learning for Materials Discovery","display_name":"Foundation-Model Surrogates Enable Data-Efficient Active Learning for Materials Discovery","publication_year":2026,"publication_date":"2026-03-13","ids":{"openalex":"https://openalex.org/W7136888862","doi":"https://doi.org/10.48550/arxiv.2603.12567"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.12567","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12567","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.2603.12567","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129545578","display_name":"Jeffrey Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Jeffrey","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129636189","display_name":"Rongzhi Dong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dong, Rongzhi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129422706","display_name":"Ying Feng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Ying","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129529356","display_name":"Ming Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Ming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129492230","display_name":"Jianjun Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Jianjun","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":0.9936000108718872,"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":0.9936000108718872,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.0015999999595806003,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0005000000237487257,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.6507999897003174},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5532000064849854},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.5479000210762024},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5297999978065491},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5109999775886536},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.46950000524520874},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.41449999809265137},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.4050000011920929}],"concepts":[{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.6507999897003174},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5532000064849854},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.5479000210762024},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5297999978065491},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5109999775886536},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5072000026702881},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4862000048160553},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.46950000524520874},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45890000462532043},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.41449999809265137},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.4050000011920929},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.39809998869895935},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.35120001435279846},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.3400000035762787},{"id":"https://openalex.org/C2781204021","wikidata":"https://www.wikidata.org/wiki/Q6497091","display_name":"Lattice (music)","level":2,"score":0.32359999418258667},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3124000132083893},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.3012999892234802},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3012000024318695},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.28139999508857727},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.2784999907016754},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2754000127315521},{"id":"https://openalex.org/C131675550","wikidata":"https://www.wikidata.org/wiki/Q7646884","display_name":"Surrogate model","level":2,"score":0.26350000500679016},{"id":"https://openalex.org/C204530211","wikidata":"https://www.wikidata.org/wiki/Q752823","display_name":"Thermal","level":2,"score":0.26260000467300415}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.12567","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12567","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.2603.12567","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12567","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":{"Active":[0,98],"learning":[1],"(AL)":[2],"has":[3],"emerged":[4],"as":[5,230],"a":[6,111,125,139,180],"powerful":[7],"paradigm":[8],"for":[9,156,233],"accelerating":[10],"materials":[11,77,168,241],"discovery":[12,238],"by":[13,105],"iteratively":[14],"steering":[15],"experiments":[16],"toward":[17],"promising":[18],"candidates,":[19],"reducing":[20],"the":[21,208,215],"number":[22],"of":[23,119,176,183],"costly":[24],"synthesis-and-characterization":[25],"cycles":[26],"needed":[27],"to":[28,57,123,189,194],"identify":[29],"optimal":[30],"materials.":[31],"However,":[32],"current":[33],"AL":[34],"relies":[35],"predominantly":[36],"on":[37,90,117,173],"Gaussian":[38],"Process":[39],"(GP)":[40],"and":[41,151,164,170,191,212,243],"Random":[42],"Forest":[43],"(RF)":[44],"surrogates,":[45],"which":[46,101,132],"suffer":[47],"from":[48,88,203],"complementary":[49],"limitations:":[50],"GP":[51,163,190],"underfits":[52],"complex":[53],"composition-property":[54],"landscapes":[55],"due":[56],"rigid":[58],"kernel":[59],"assumptions,":[60],"while":[61],"RF":[62,165],"produces":[63],"unreliable":[64],"heuristic":[65],"uncertainty":[66,154,205],"estimates":[67],"in":[68,76,138,185],"small-data":[69,72,148,244],"regimes.":[70],"This":[71],"challenge":[73],"is":[74],"pervasive":[75],"science,":[78],"making":[79],"reliable":[80],"surrogate":[81],"modeling":[82],"extremely":[83],"difficult":[84],"with":[85,109],"models":[86],"trained":[87],"scratch":[89],"each":[91],"new":[92],"dataset.":[93],"Here":[94],"we":[95],"propose":[96],"In-Context":[97],"Learning":[99],"(ICAL),":[100],"addresses":[102],"this":[103],"bottleneck":[104],"replacing":[106],"conventional":[107],"surrogates":[108,232],"TabPFN,":[110],"transformer-based":[112],"foundation":[113],"model":[114],"(FM)":[115],"pre-trained":[116,226],"millions":[118],"synthetic":[120],"regression":[121,149],"tasks":[122],"meta-learn":[124],"universal":[126],"prior":[127],"over":[128],"tabular":[129],"data,":[130],"upon":[131],"TabPFN":[133,171],"performs":[134],"principled":[135],"Bayesian":[136],"inference":[137],"single":[140],"forward":[141],"pass":[142],"without":[143],"dataset-specific":[144],"retraining,":[145],"delivering":[146],"strong":[147],"performance":[150],"well-calibrated":[152],"predictive":[153],"(required":[155],"effective":[157,231],"AL).":[158],"We":[159],"benchmark":[160],"ICAL":[161],"against":[162],"across":[166,239],"10":[167,177],"datasets":[169],"wins":[172],"8":[174],"out":[175],"datasets,":[178],"achieving":[179,207],"mean":[181],"saving":[182],"52%":[184],"extra":[186],"evaluations":[187],"relative":[188,193],"29.77%":[192],"RF.":[195],"Cross-validation":[196],"analysis":[197],"confirms":[198],"that":[199,225],"TabPFN's":[200],"advantage":[201],"stems":[202],"superior":[204],"calibration,":[206],"lowest":[209],"Negative":[210],"Log-Likelihood":[211],"Area":[213],"Under":[214],"Sparsification":[216],"Error":[217],"curve":[218],"among":[219],"all":[220],"surrogates.":[221],"These":[222],"results":[223],"demonstrate":[224],"FMs":[227],"can":[228],"serve":[229],"active":[234],"learning,":[235],"enabling":[236],"data-efficient":[237],"diverse":[240],"systems":[242],"experimental":[245],"sciences.":[246]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-17T00:00:00"}
