{"id":"https://openalex.org/W7117651127","doi":"https://doi.org/10.48550/arxiv.2512.22740","title":"Multi-Task Learning for Metal Alloy Property Prediction: An Empirical Study of Negative Transfer and Mitigation Strategies","display_name":"Multi-Task Learning for Metal Alloy Property Prediction: An Empirical Study of Negative Transfer and Mitigation Strategies","publication_year":2025,"publication_date":"2025-12-28","ids":{"openalex":"https://openalex.org/W7117651127","doi":"https://doi.org/10.48550/arxiv.2512.22740"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2512.22740","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.22740","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2512.22740","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5121632540","display_name":"Sungwoo Kang","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Kang, Sungwoo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5121632540"],"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":0.876800000667572,"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":0.876800000667572,"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/T14347","display_name":"Big Data and Digital Economy","score":0.009800000116229057,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.008899999782443047,"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/property","display_name":"Property (philosophy)","score":0.5812000036239624},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.5540000200271606},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4681999981403351},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.46639999747276306},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.4625999927520752},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.4496999979019165},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.43689998984336853},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43529999256134033},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4307999908924103}],"concepts":[{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.5812000036239624},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.5540000200271606},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49140000343322754},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4681999981403351},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.46639999747276306},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.4625999927520752},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.4496999979019165},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4383000135421753},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.43689998984336853},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43529999256134033},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4307999908924103},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.42829999327659607},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4260999858379364},{"id":"https://openalex.org/C63817138","wikidata":"https://www.wikidata.org/wiki/Q3455889","display_name":"Quantile regression","level":2,"score":0.4007999897003174},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.3781999945640564},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.36629998683929443},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3643999993801117},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3578999936580658},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3343999981880188},{"id":"https://openalex.org/C120068334","wikidata":"https://www.wikidata.org/wiki/Q45343","display_name":"Polynomial regression","level":3,"score":0.3269999921321869},{"id":"https://openalex.org/C2780026712","wikidata":"https://www.wikidata.org/wiki/Q37756","display_name":"Alloy","level":2,"score":0.3089999854564667},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.29829999804496765},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.29750001430511475},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.2915000021457672},{"id":"https://openalex.org/C90119067","wikidata":"https://www.wikidata.org/wiki/Q43260","display_name":"Polynomial","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.2791999876499176},{"id":"https://openalex.org/C133199616","wikidata":"https://www.wikidata.org/wiki/Q25386885","display_name":"Empirical modelling","level":2,"score":0.2791999876499176},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2687999904155731},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2565999925136566}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2512.22740","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.22740","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2512.22740","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.22740","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"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":{"Multi-task":[0],"learning":[1],"(MTL)":[2],"in":[3],"materials":[4],"science":[5],"relies":[6],"on":[7],"the":[8,99],"assumption":[9,20],"that":[10,82,134],"physically":[11],"related":[12],"properties":[13],"share":[14],"learnable":[15],"representations.":[16],"We":[17,53],"challenge":[18],"this":[19,55],"using":[21],"a":[22,34,106,128],"54,028-sample":[23],"metal":[24],"alloy":[25],"dataset":[26],"exhibiting":[27],"extreme":[28],"task-level":[29],"imbalance.":[30],"Our":[31],"results":[32],"reveal":[33],"striking":[35],"dichotomy:":[36],"MTL":[37,117],"significantly":[38],"degrades":[39],"regression":[40],"performance":[41],"for":[42,50,112,118],"resistivity":[43],"and":[44],"hardness":[45],"but":[46,115],"improves":[47],"classification":[48],"recall":[49,122],"amorphous-forming":[51],"ability.":[52],"trace":[54],"divergence":[56],"to":[57,142],"mismatched":[58],"functional":[59],"forms--such":[60],"as":[61],"resistivity's":[62],"polynomial":[63],"dependence":[64],"versus":[65],"hardness's":[66],"complex":[67],"interactions--which":[68],"cause":[69],"severe":[70],"gradient":[71,96],"misalignment":[72],"during":[73],"optimization.":[74],"Evaluating":[75],"Deep":[76],"Imbalanced":[77],"Regression":[78],"techniques,":[79],"we":[80,104],"find":[81],"projecting":[83],"conflicting":[84],"gradients":[85],"(PCGrad)":[86],"recovers":[87],"single-task":[88],"performance,":[89],"while":[90],"combining":[91],"label":[92],"distribution":[93],"smoothing":[94],"with":[95],"normalization":[97],"achieves":[98],"best":[100],"overall":[101],"balance.":[102],"Consequently,":[103],"propose":[105],"strategic":[107],"framework:":[108],"utilize":[109],"independent":[110],"models":[111],"high-precision":[113],"characterization,":[114],"employ":[116],"high-throughput":[119],"screening":[120],"where":[121],"is":[123],"paramount.":[124],"These":[125],"findings":[126],"support":[127],"\"materials":[129],"property":[130],"clustering\"":[131],"hypothesis,":[132],"suggesting":[133],"distinct":[135],"physical":[136],"mechanisms":[137],"require":[138],"specialized":[139],"optimization":[140],"strategies":[141],"overcome":[143],"negative":[144],"transfer.":[145]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-12-31T00:00:00"}