{"id":"https://openalex.org/W7224595476","doi":"https://doi.org/10.26434/chemrxiv.15011216/v1","title":"IMPLICIT LEARNING: REACTION OUTCOME PREDICTION FROM POSITIVE-ONLY DATA","display_name":"IMPLICIT LEARNING: REACTION OUTCOME PREDICTION FROM POSITIVE-ONLY DATA","publication_year":2026,"publication_date":"2026-10-28","ids":{"openalex":"https://openalex.org/W7224595476","doi":"https://doi.org/10.26434/chemrxiv.15011216/v1"},"language":"en","primary_location":{"id":"doi:10.26434/chemrxiv.15011216/v1","doi":"https://doi.org/10.26434/chemrxiv.15011216/v1","is_oa":true,"landing_page_url":"https://doi.org/10.26434/chemrxiv.15011216/v1","pdf_url":"https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15011216/v1","source":{"id":"https://openalex.org/S4393918830","display_name":"ChemRxiv","issn_l":"2573-2293","issn":["2573-2293"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"volume":null,"type":"preprint","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15011216/v1","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5000844400","display_name":"Nick Wierich","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I22465464","display_name":"University of M\u00fcnster","ror":"https://ror.org/00pd74e08","country_code":"DE","type":"education","lineage":["https://openalex.org/I22465464"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Nick Wierich","raw_affiliation_strings":["Organisch-Chemisches-Institut Universit\u00e4t M\u00fcnster 48149 M\u00fcnster , Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Organisch-Chemisches-Institut Universit\u00e4t M\u00fcnster 48149 M\u00fcnster , Germany","institution_ids":["https://openalex.org/I22465464"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138532291","display_name":"Niklas Dobberstein","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I22465464","display_name":"University of M\u00fcnster","ror":"https://ror.org/00pd74e08","country_code":"DE","type":"education","lineage":["https://openalex.org/I22465464"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Niklas Dobberstein","raw_affiliation_strings":["Organisch-Chemisches-Institut Universit\u00e4t M\u00fcnster 48149 M\u00fcnster , Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Organisch-Chemisches-Institut Universit\u00e4t M\u00fcnster 48149 M\u00fcnster , Germany","institution_ids":["https://openalex.org/I22465464"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017167322","display_name":"Frank Glorius","orcid":"https://orcid.org/0000-0002-0648-956X","observed_orcids":["https://orcid.org/0000-0002-0648-956X"]},"institutions":[{"id":"https://openalex.org/I22465464","display_name":"University of M\u00fcnster","ror":"https://ror.org/00pd74e08","country_code":"DE","type":"education","lineage":["https://openalex.org/I22465464"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Frank Glorius","raw_affiliation_strings":["Organisch-Chemisches-Institut Universit\u00e4t M\u00fcnster 48149 M\u00fcnster , Germany"],"raw_orcid":"https://orcid.org/0000-0002-0648-956X","affiliations":[{"raw_affiliation_string":"Organisch-Chemisches-Institut Universit\u00e4t M\u00fcnster 48149 M\u00fcnster , Germany","institution_ids":["https://openalex.org/I22465464"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I22465464"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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":0.7484999895095825,"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":0.7484999895095825,"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/T10211","display_name":"Computational Drug Discovery Methods","score":0.23579999804496765,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T13297","display_name":"History and advancements in chemistry","score":0.0027000000700354576,"subfield":{"id":"https://openalex.org/subfields/1606","display_name":"Physical and Theoretical 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/reaction-prediction","display_name":"chemical reaction prediction","score":0.9380000233650208},{"id":"https://openalex.org/keywords/high-throughput-experimentation","display_name":"high-throughput experimentation","score":0.6940000057220459}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6970000267028809},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.5547000169754028},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5544999837875366},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5526000261306763},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5428000092506409},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5317999720573425},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5084999799728394},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.41999998688697815},{"id":"https://openalex.org/C2779561248","wikidata":"https://www.wikidata.org/wiki/Q743861","display_name":"Tacit knowledge","level":2,"score":0.36500000953674316},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29840001463890076},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.2782999873161316}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.26434/chemrxiv.15011216/v1","doi":"https://doi.org/10.26434/chemrxiv.15011216/v1","is_oa":true,"landing_page_url":"https://doi.org/10.26434/chemrxiv.15011216/v1","pdf_url":"https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15011216/v1","source":{"id":"https://openalex.org/S4393918830","display_name":"ChemRxiv","issn_l":"2573-2293","issn":["2573-2293"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.26434/chemrxiv.15011216/v1","doi":"https://doi.org/10.26434/chemrxiv.15011216/v1","is_oa":true,"landing_page_url":"https://doi.org/10.26434/chemrxiv.15011216/v1","pdf_url":"https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15011216/v1","source":{"id":"https://openalex.org/S4393918830","display_name":"ChemRxiv","issn_l":"2573-2293","issn":["2573-2293"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7224595476.pdf","grobid_xml":"https://content.openalex.org/works/W7224595476.grobid-xml"},"referenced_works_count":6,"referenced_works":["https://openalex.org/W2604272262","https://openalex.org/W2753921001","https://openalex.org/W2907182766","https://openalex.org/W3170676482","https://openalex.org/W4416038707","https://openalex.org/W6921049458"],"related_works":["https://openalex.org/W4411259040","https://openalex.org/W4413331274","https://openalex.org/W7130726821","https://openalex.org/W4398765582","https://openalex.org/W4415274987","https://openalex.org/W6893830417","https://openalex.org/W3137928831","https://openalex.org/W7204819786","https://openalex.org/W4400583643","https://openalex.org/W4231645939"],"abstract_inverted_index":{"Machine":[0],"learning":[1],"models":[2,27,142],"for":[3],"reaction":[4,76,101,139,166],"product":[5],"prediction":[6,141],"have":[7],"largely":[8],"been":[9],"trained":[10,116],"and":[11,34,65,126],"evaluated":[12,138],"on":[13,117,152],"datasets":[14,88],"of":[15,41,74,94],"successful":[16,33,145],"reactions.":[17,148],"While":[18,71,115],"this":[19],"setting":[20],"allows":[21],"reaching":[22],"high":[23],"accuracy":[24,151],"scores,":[25],"these":[26],"generally":[28],"struggle":[29],"to":[30,38,61,80,122,163],"discriminate":[31],"between":[32,124],"unsuccessful":[35],"transformations":[36],"due":[37],"the":[39,69,72,153],"lack":[40],"negative":[42,75,113,165],"training":[43],"data.":[44],"This":[45],"critically":[46],"limits":[47],"their":[48],"practical":[49],"use,":[50],"as":[51],"reliably":[52],"recognizing":[53],"unproductive":[54,127],"substrate":[55],"combinations":[56],"in":[57,68,98,143],"silico":[58],"is":[59],"required":[60],"accelerate":[62],"experimental":[63],"campaigns":[64],"save":[66],"resources":[67],"laboratory.":[70],"availability":[73],"data":[77],"remains":[78,157],"limited":[79],"date,":[81],"we":[82,106],"show":[83],"that":[84],"even":[85],"purely":[86],"positive":[87],"implicitly":[89],"contain":[90],"a":[91],"significant":[92],"amount":[93],"information":[95],"about":[96],"cases":[97],"which":[99],"no":[100],"occurs.":[102],"To":[103],"exploit":[104],"this,":[105],"introduce":[107],"TACIT":[108,120],"(TAckling":[109],"Chemistry":[110],"through":[111],"ImpliciT":[112],"learning).":[114],"positive-only":[118],"data,":[119],"learns":[121],"distinguish":[123],"productive":[125],"transformations.":[128],"On":[129],"two":[130],"published":[131],"high-throughput":[132],"experimentation":[133],"datasets,":[134],"it":[135],"outperforms":[136],"all":[137],"outcome":[140],"separating":[144],"from":[146],"failed":[147],"Crucially,":[149],"TACIT\u2019s":[150],"popular":[154],"USPTO-MIT":[155],"benchmark":[156],"competitive,":[158],"despite":[159],"its":[160],"added":[161],"ability":[162],"predict":[164],"outcomes.":[167]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2026-10-30T00:00:00"}
