{"id":"https://openalex.org/W7148673980","doi":"https://doi.org/10.48550/arxiv.2604.01328","title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","display_name":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","publication_year":2026,"publication_date":"2026-04-01","ids":{"openalex":"https://openalex.org/W7148673980","doi":"https://doi.org/10.48550/arxiv.2604.01328"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.01328","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01328","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.01328","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5110289457","display_name":"Zhongwei Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yu, Zhongwei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000651751","display_name":"Rasul Tutunov","orcid":"https://orcid.org/0000-0002-6495-6437"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tutunov, Rasul","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018539132","display_name":"Alexandre Maraval","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Maraval, Alexandre Max","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002028885","display_name":"Zikai Xie","orcid":"https://orcid.org/0009-0006-5125-1880"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Zikai","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132835799","display_name":"Zhenzhi Tan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tan, Zhenzhi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132859609","display_name":"Jiankang Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jiankang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073920194","display_name":"Zijing Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Bin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132903562","display_name":"Liangliang Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zijing","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132908965","display_name":"Qi Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Liangliang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Yang, Qi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Qi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Jiang, Jun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Jun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Luo, Sanzhong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Sanzhong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Guo, Zhenxiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Zhenxiao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Zhang, Tongyi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Tongyi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Bou-Ammar, Haitham","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bou-Ammar, Haitham","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Wang, Jun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jun","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":16,"corresponding_author_ids":["https://openalex.org/A5110289457"],"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":0.6996999979019165,"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":0.6996999979019165,"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/T11986","display_name":"Scientific Computing and Data Management","score":0.0754999965429306,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.06270000338554382,"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/scientific-discovery","display_name":"Scientific discovery","score":0.6021000146865845},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.49729999899864197},{"id":"https://openalex.org/keywords/cover","display_name":"Cover (algebra)","score":0.4683000147342682},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.45579999685287476},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.436599999666214},{"id":"https://openalex.org/keywords/core","display_name":"Core (optical fiber)","score":0.41130000352859497}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7315999865531921},{"id":"https://openalex.org/C2984917352","wikidata":"https://www.wikidata.org/wiki/Q12772819","display_name":"Scientific discovery","level":2,"score":0.6021000146865845},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.49729999899864197},{"id":"https://openalex.org/C2780428219","wikidata":"https://www.wikidata.org/wiki/Q16952335","display_name":"Cover (algebra)","level":2,"score":0.4683000147342682},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.45579999685287476},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4458000063896179},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.436599999666214},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42100000381469727},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.41130000352859497},{"id":"https://openalex.org/C138379479","wikidata":"https://www.wikidata.org/wiki/Q1116876","display_name":"Scientific modelling","level":2,"score":0.3977000117301941},{"id":"https://openalex.org/C2780366209","wikidata":"https://www.wikidata.org/wiki/Q5170200","display_name":"Core model","level":2,"score":0.3619999885559082},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.326200008392334},{"id":"https://openalex.org/C23213167","wikidata":"https://www.wikidata.org/wiki/Q2351730","display_name":"Scientific progress","level":2,"score":0.3165999948978424},{"id":"https://openalex.org/C2781083858","wikidata":"https://www.wikidata.org/wiki/Q17327049","display_name":"Scientific literature","level":2,"score":0.3158000111579895},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2705000042915344},{"id":"https://openalex.org/C539667460","wikidata":"https://www.wikidata.org/wiki/Q2414942","display_name":"Management science","level":1,"score":0.26910001039505005}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.01328","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01328","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.01328","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01328","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":"article"},"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":{"Traditional":[0],"scientific":[1,46,88,123,166],"discovery":[2,89],"relies":[3],"on":[4],"an":[5,91],"iterative":[6],"hypothesise-experiment-refine":[7],"cycle":[8],"that":[9,40],"has":[10],"driven":[11],"progress":[12],"for":[13,122,135],"centuries,":[14],"but":[15],"its":[16],"intuitive,":[17],"ad-hoc":[18],"implementation":[19],"often":[20],"wastes":[21],"resources,":[22],"yields":[23],"inefficient":[24],"designs,":[25],"and":[26,42,62,74,82,101,113,131,163],"misses":[27],"critical":[28,119],"insights.":[29],"This":[30],"tutorial":[31,140],"presents":[32],"Bayesian":[33],"Optimisation":[34],"(BO),":[35],"a":[36,136],"principled":[37,165],"probability-driven":[38],"framework":[39],"formalises":[41],"automates":[43],"this":[44,139],"core":[45,97],"cycle.":[47],"BO":[48,145],"uses":[49],"surrogate":[50],"models":[51],"(e.g.,":[52],"Gaussian":[53],"processes)":[54],"to":[55,65,79,154,158],"model":[56],"empirical":[57],"observations":[58],"as":[59,90],"evolving":[60],"hypotheses,":[61],"acquisition":[63],"functions":[64],"guide":[66],"experiment":[67],"selection,":[68],"balancing":[69],"exploitation":[70],"of":[71,76],"known":[72],"knowledge":[73],"exploration":[75],"uncharted":[77],"domains":[78],"eliminate":[80],"guesswork":[81],"manual":[83],"trial-and-error.":[84],"We":[85,116],"first":[86],"frame":[87],"optimisation":[92],"problem,":[93],"then":[94],"unpack":[95],"BO's":[96],"components,":[98],"end-to-end":[99],"workflows,":[100],"real-world":[102],"efficacy":[103],"via":[104],"case":[105],"studies":[106],"in":[107,144],"catalysis,":[108],"materials":[109],"science,":[110],"organic":[111],"synthesis,":[112],"molecule":[114],"discovery.":[115,167],"also":[117],"cover":[118],"technical":[120],"extensions":[121],"applications,":[124,150],"including":[125],"batched":[126],"experimentation,":[127],"heteroscedasticity,":[128],"contextual":[129],"optimisation,":[130],"human-in-the-loop":[132],"integration.":[133],"Tailored":[134],"broad":[137],"audience,":[138],"bridges":[141],"AI":[142],"advances":[143],"with":[146],"practical":[147],"natural":[148],"science":[149],"offering":[151],"tiered":[152],"content":[153],"empower":[155],"cross-disciplinary":[156],"researchers":[157],"design":[159],"more":[160],"efficient":[161],"experiments":[162],"accelerate":[164]},"counts_by_year":[],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2026-04-04T00:00:00"}