{"id":"https://openalex.org/W4411646464","doi":"https://doi.org/10.1021/acs.jcim.5c00550","title":"Assessing Uncertainty in Machine Learning for Polymer Property Prediction: A Benchmark Study","display_name":"Assessing Uncertainty in Machine Learning for Polymer Property Prediction: A Benchmark Study","publication_year":2025,"publication_date":"2025-06-25","ids":{"openalex":"https://openalex.org/W4411646464","doi":"https://doi.org/10.1021/acs.jcim.5c00550","pmid":"https://pubmed.ncbi.nlm.nih.gov/40560148"},"language":"en","primary_location":{"id":"doi:10.1021/acs.jcim.5c00550","is_oa":false,"landing_page_url":"https://doi.org/10.1021/acs.jcim.5c00550","pdf_url":null,"source":{"id":"https://openalex.org/S167262187","display_name":"Journal of Chemical Information and Modeling","issn_l":"1549-9596","issn":["1549-9596","1549-960X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320006","host_organization_name":"American Chemical Society","host_organization_lineage":["https://openalex.org/P4310320006"],"host_organization_lineage_names":["American Chemical Society"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Chemical Information and Modeling","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101131916","display_name":"Hao Ting-yue","orcid":"https://orcid.org/0009-0009-4337-5164"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hao Tang","raw_affiliation_strings":["Department of Mechanical Engineering","University of Wisconsin-Madison"],"affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering","institution_ids":[]},{"raw_affiliation_string":"University of Wisconsin-Madison","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102863432","display_name":"Tianle Yue","orcid":"https://orcid.org/0009-0005-3390-1344"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tianle Yue","raw_affiliation_strings":["Department of Mechanical Engineering","University of Wisconsin-Madison"],"affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering","institution_ids":[]},{"raw_affiliation_string":"University of Wisconsin-Madison","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100414156","display_name":"Ying Li","orcid":"https://orcid.org/0000-0002-1487-3350"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ying Li","raw_affiliation_strings":["Department of Mechanical Engineering","University of Wisconsin-Madison"],"affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering","institution_ids":[]},{"raw_affiliation_string":"University of Wisconsin-Madison","institution_ids":["https://openalex.org/I135310074"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100414156"],"corresponding_institution_ids":["https://openalex.org/I135310074"],"apc_list":null,"apc_paid":null,"fwci":3.876,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.93976066,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"65","issue":"13","first_page":"6585","last_page":"6598"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9876000285148621,"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":0.9876000285148621,"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":0.9390000104904175,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7735990285873413},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.6905806660652161},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.595862090587616},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.583035945892334},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5150849223136902},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.06641992926597595}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7735990285873413},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.6905806660652161},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.595862090587616},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.583035945892334},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5150849223136902},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.06641992926597595},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009010","descriptor_name":"Monte Carlo Method","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009010","descriptor_name":"Monte Carlo Method","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011108","descriptor_name":"Polymers","qualifier_ui":"Q000737","qualifier_name":"chemistry","is_major_topic":true},{"descriptor_ui":"D011108","descriptor_name":"Polymers","qualifier_ui":"Q000737","qualifier_name":"chemistry","is_major_topic":true},{"descriptor_ui":"D019985","descriptor_name":"Benchmarking","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D019985","descriptor_name":"Benchmarking","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D035501","descriptor_name":"Uncertainty","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D035501","descriptor_name":"Uncertainty","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":2,"locations":[{"id":"doi:10.1021/acs.jcim.5c00550","is_oa":false,"landing_page_url":"https://doi.org/10.1021/acs.jcim.5c00550","pdf_url":null,"source":{"id":"https://openalex.org/S167262187","display_name":"Journal of Chemical Information and Modeling","issn_l":"1549-9596","issn":["1549-9596","1549-960X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320006","host_organization_name":"American Chemical Society","host_organization_lineage":["https://openalex.org/P4310320006"],"host_organization_lineage_names":["American Chemical Society"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Chemical Information and Modeling","raw_type":"journal-article"},{"id":"pmid:40560148","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40560148","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of chemical information and modeling","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1031719595","display_name":null,"funder_award_id":"FA8650-20-S-5008","funder_id":"https://openalex.org/F4320338510","funder_display_name":"Materials and Manufacturing Directorate"},{"id":"https://openalex.org/G4460964086","display_name":null,"funder_award_id":"2314424","funder_id":"https://openalex.org/F4320337391","funder_display_name":"Division of Civil, Mechanical and Manufacturing Innovation"},{"id":"https://openalex.org/G6704618686","display_name":null,"funder_award_id":"2323108","funder_id":"https://openalex.org/F4320337391","funder_display_name":"Division of Civil, Mechanical and Manufacturing Innovation"},{"id":"https://openalex.org/G6941650839","display_name":null,"funder_award_id":"FA9550-20-1-0183","funder_id":"https://openalex.org/F4320338279","funder_display_name":"Air Force Office of Scientific Research"},{"id":"https://openalex.org/G8559326691","display_name":null,"funder_award_id":"2316200","funder_id":"https://openalex.org/F4320337391","funder_display_name":"Division of Civil, Mechanical and Manufacturing Innovation"}],"funders":[{"id":"https://openalex.org/F4320306555","display_name":"Wisconsin Alumni Research Foundation","ror":"https://ror.org/00hwxbz16"},{"id":"https://openalex.org/F4320337391","display_name":"Division of Civil, Mechanical and Manufacturing Innovation","ror":"https://ror.org/028yd4c30"},{"id":"https://openalex.org/F4320338279","display_name":"Air Force Office of Scientific Research","ror":"https://ror.org/011e9bt93"},{"id":"https://openalex.org/F4320338510","display_name":"Materials and Manufacturing Directorate","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":69,"referenced_works":["https://openalex.org/W1560021816","https://openalex.org/W1904365287","https://openalex.org/W1975147762","https://openalex.org/W1980328470","https://openalex.org/W1987779096","https://openalex.org/W1994005439","https://openalex.org/W2006571183","https://openalex.org/W2038702914","https://openalex.org/W2072976471","https://openalex.org/W2092368765","https://openalex.org/W2098824882","https://openalex.org/W2100805904","https://openalex.org/W2135293965","https://openalex.org/W2153451628","https://openalex.org/W2604272474","https://openalex.org/W2789710473","https://openalex.org/W2789758093","https://openalex.org/W2883528235","https://openalex.org/W2883583109","https://openalex.org/W2891197287","https://openalex.org/W2904181725","https://openalex.org/W2945551948","https://openalex.org/W2949676527","https://openalex.org/W2951450280","https://openalex.org/W2952832141","https://openalex.org/W2963613699","https://openalex.org/W2984886967","https://openalex.org/W2987395105","https://openalex.org/W2990138404","https://openalex.org/W3005714846","https://openalex.org/W3010244463","https://openalex.org/W3014339631","https://openalex.org/W3022711947","https://openalex.org/W3036490780","https://openalex.org/W3043694802","https://openalex.org/W3044724994","https://openalex.org/W3049695724","https://openalex.org/W3089611792","https://openalex.org/W3093859831","https://openalex.org/W3119329252","https://openalex.org/W3126878921","https://openalex.org/W3129778969","https://openalex.org/W3142188241","https://openalex.org/W3150113257","https://openalex.org/W3156578609","https://openalex.org/W3183048323","https://openalex.org/W3185456481","https://openalex.org/W3189164715","https://openalex.org/W3194649466","https://openalex.org/W3207749172","https://openalex.org/W4241653265","https://openalex.org/W4246239401","https://openalex.org/W4281994895","https://openalex.org/W4315628777","https://openalex.org/W4317215121","https://openalex.org/W4365452029","https://openalex.org/W4377820623","https://openalex.org/W4387078269","https://openalex.org/W4387762676","https://openalex.org/W4389471479","https://openalex.org/W4391786167","https://openalex.org/W4392896772","https://openalex.org/W4394845241","https://openalex.org/W4400008163","https://openalex.org/W4400317808","https://openalex.org/W4401461011","https://openalex.org/W4401612000","https://openalex.org/W4401628769","https://openalex.org/W4401694629"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Machine":[0],"learning":[1,86],"(ML)":[2],"has":[3],"emerged":[4,212],"as":[5,41,213],"a":[6,50,134,168,202],"transformative":[7],"tool":[8],"in":[9,37,57,233],"material":[10],"science,":[11],"enabling":[12],"accelerated":[13],"discovery":[14,277],"and":[15,78,110,130,142,156,163,208,223,240,273],"design":[16],"of":[17,33,53,150,205,257,266,278],"novel":[18],"molecules":[19],"while":[20],"reducing":[21],"experimental":[22,155,270],"costs.":[23],"Uncertainty":[24],"quantification":[25],"(UQ)":[26],"is":[27,182],"crucial":[28],"for":[29,137,189,217],"enhancing":[30],"the":[31,214,244,254,264,276],"reliability":[32],"ML":[34,267],"predictions,":[35,268],"particularly":[36],"high-stakes":[38],"applications,":[39],"such":[40],"functional":[42,280],"polymer":[43,97,165,247],"discovery.":[44],"In":[45],"this":[46,234],"study,":[47],"we":[48],"present":[49],"comprehensive":[51,251],"evaluation":[52],"nine":[54,245],"UQ":[55,179,260],"methods":[56],"ML\u2500ensemble,":[58],"Gaussian":[59],"Process":[60],"Regression":[61],"(GPR),":[62],"Monte":[63,81],"Carlo":[64,82],"Dropout":[65],"(MCD),":[66],"mean-variance":[67],"estimation":[68],"(MVE),":[69],"Bayesian":[70],"Neural":[71],"Network":[72],"based":[73],"on":[74,171],"Variational":[75],"Inference":[76],"(BNN-VI)":[77],"Markov":[79],"Chain":[80],"(BNN-MCMC),":[83],"evidential":[84],"deep":[85],"(EDL),":[87],"quantile":[88],"regression":[89],"(QR),":[90],"natural":[91],"gradient":[92],"boosting":[93],"(NGBoost)\u2500for":[94],"predicting":[95],"key":[96],"properties,":[98,152],"including":[99,122],"glass":[100],"transition":[101],"temperature":[102,108,112],"(<i>T</i><sub>g</sub>),":[103],"band":[104],"gap":[105],"(<i>E</i><sub>g</sub>),":[106],"melting":[107],"(<i>T</i><sub>m</sub>)":[109],"decomposition":[111],"(<i>T</i><sub>d</sub>).":[113],"The":[114],"models":[115],"are":[116],"assessed":[117],"using":[118],"three":[119],"independent":[120],"metrics,":[121],"prediction":[123],"accuracy":[124,207,222,232],"(<i>R</i><sup>2</sup>),":[125],"Spearman's":[126],"rank":[127],"correlation":[128],"coefficient":[129],"calibration":[131],"area,":[132],"offering":[133],"robust":[135],"framework":[136],"evaluating":[138],"both":[139],"mean":[140],"predictions":[141,192],"uncertainty":[143,224],"estimates.":[144],"Our":[145,174],"analysis":[146],"spans":[147],"data":[148],"sets":[149],"four":[151,194],"out-of-distribution":[153],"(OOD)":[154],"molecular":[157],"dynamics":[158],"(MD)-derived":[159],"data,":[160],"high-<i>T</i><sub>g</sub>":[161,218],"polymers":[162],"diverse":[164],"types,":[166],"providing":[167,230],"holistic":[169],"perspective":[170],"model":[172],"performance.":[173],"findings":[175],"reveal":[176],"that":[177],"optimal":[178],"method":[180,186,216,228],"selection":[181],"highly":[183],"context-dependent.":[184],"Ensemble":[185,227],"consistently":[187],"excelled":[188],"general":[190],"in-distribution":[191],"across":[193,243],"properties.":[195],"For":[196],"challenging":[197],"OOD":[198],"scenarios,":[199],"BNN-MCMC":[200],"offered":[201],"strong":[203],"balance":[204],"predictive":[206],"reliable":[209],"UQ.":[210],"NGBoost":[211],"top-performing":[215],"polymers,":[219],"effectively":[220],"balancing":[221],"characterization,":[225],"with":[226],"also":[229],"excellent":[231],"case.":[235],"Furthermore,":[236],"BNN-VI":[237],"demonstrated":[238],"superior":[239],"consistent":[241],"performance":[242],"distinct":[246],"classes":[248],"evaluated.":[249],"This":[250],"benchmark":[252],"underscores":[253],"critical":[255],"importance":[256],"selecting":[258],"tailored":[259],"strategies":[261],"to":[262],"enhance":[263],"trustworthiness":[265],"optimize":[269],"validation":[271],"efforts,":[272],"ultimately":[274],"accelerate":[275],"advanced":[279],"polymers.":[281]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":3}],"updated_date":"2026-03-17T09:09:15.849793","created_date":"2025-10-10T00:00:00"}