{"id":"https://openalex.org/W4407205293","doi":"https://doi.org/10.36922/ijamd.5744","title":"Predicting effective thermal conductivity of sintered nano-Ag with artificial neural networks","display_name":"Predicting effective thermal conductivity of sintered nano-Ag with artificial neural networks","publication_year":2025,"publication_date":"2025-02-06","ids":{"openalex":"https://openalex.org/W4407205293","doi":"https://doi.org/10.36922/ijamd.5744"},"language":"en","primary_location":{"id":"doi:10.36922/ijamd.5744","doi":"https://doi.org/10.36922/ijamd.5744","is_oa":true,"landing_page_url":"https://doi.org/10.36922/ijamd.5744","pdf_url":null,"source":{"id":"https://openalex.org/S5407045070","display_name":"International Journal of AI for Materials and Design","issn_l":"3029-2573","issn":["3029-2573","3041-0746"],"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":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of AI for Materials and Design","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.36922/ijamd.5744","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Libo Zhao","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Libo Zhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100560136","display_name":"Jiahui Wei","orcid":"https://orcid.org/0000-0002-3384-5961","observed_orcids":["https://orcid.org/0000-0002-3384-5961"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiahui Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048697440","display_name":"Yanwei Dai","orcid":"https://orcid.org/0000-0002-2636-772X","observed_orcids":["https://orcid.org/0000-0002-2636-772X"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yanwei Dai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104009626","display_name":"Daowei Wu","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Daowei Wu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Yuting Zhang","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuting Zhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100749577","display_name":"Kui Li","orcid":"https://orcid.org/0000-0002-9892-1283","observed_orcids":["https://orcid.org/0000-0002-9892-1283"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kui Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5101940032","display_name":"Fei Hu Qin","orcid":"https://orcid.org/0000-0002-5732-7827","observed_orcids":["https://orcid.org/0000-0002-5732-7827"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fei Qin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5048697440"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5618,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.51871542,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":"2","issue":"1","first_page":"8","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11277","display_name":"Thermal properties of materials","score":0.6919000148773193,"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/T11277","display_name":"Thermal properties of materials","score":0.6919000148773193,"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/T10460","display_name":"Electronic Packaging and Soldering Technologies","score":0.22669999301433563,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.03269999846816063,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/effective-thermal-conductivity","display_name":"effective thermal conductivity","score":0.8309999704360962},{"id":"https://openalex.org/keywords/neural-network","display_name":"artificial neural networks","score":0.8109999895095825},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.9789999723434448},{"id":"https://openalex.org/keywords/microstructure-characterization","display_name":"microstructure characterization","score":0.800000011920929},{"id":"https://openalex.org/keywords/scanning-electron-microscopy-image-analysis","display_name":"scanning electron microscopy image analysis","score":0.7839999794960022},{"id":"https://openalex.org/keywords/heat-transfer","display_name":"heat transfer","score":0.5799999833106995},{"id":"https://openalex.org/keywords/microstructural-parameters","display_name":"microstructural parameters","score":0.4449999928474426}],"concepts":[{"id":"https://openalex.org/C2780357685","wikidata":"https://www.wikidata.org/wiki/Q154357","display_name":"Nano-","level":2,"score":0.7718096971511841},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7472848892211914},{"id":"https://openalex.org/C97346530","wikidata":"https://www.wikidata.org/wiki/Q487005","display_name":"Thermal conductivity","level":2,"score":0.6204587817192078},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.5700712203979492},{"id":"https://openalex.org/C171250308","wikidata":"https://www.wikidata.org/wiki/Q11468","display_name":"Nanotechnology","level":1,"score":0.34266650676727295},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33565956354141235},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.2525046467781067},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.24149379134178162}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.36922/ijamd.5744","doi":"https://doi.org/10.36922/ijamd.5744","is_oa":true,"landing_page_url":"https://doi.org/10.36922/ijamd.5744","pdf_url":null,"source":{"id":"https://openalex.org/S5407045070","display_name":"International Journal of AI for Materials and Design","issn_l":"3029-2573","issn":["3029-2573","3041-0746"],"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":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of AI for Materials and Design","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.36922/ijamd.5744","doi":"https://doi.org/10.36922/ijamd.5744","is_oa":true,"landing_page_url":"https://doi.org/10.36922/ijamd.5744","pdf_url":null,"source":{"id":"https://openalex.org/S5407045070","display_name":"International Journal of AI for Materials and Design","issn_l":"3029-2573","issn":["3029-2573","3041-0746"],"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":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of AI for Materials and Design","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4308943827","https://openalex.org/W4317494848","https://openalex.org/W2914572130","https://openalex.org/W3013514471","https://openalex.org/W3088009065","https://openalex.org/W4402743931","https://openalex.org/W2969283312","https://openalex.org/W4401263436","https://openalex.org/W4383226805","https://openalex.org/W2795328348"],"abstract_inverted_index":{"Due":[0],"to":[1,76,90,139,198],"the":[2,42,80,92,127,146,154,169,190,199],"demand":[3],"for":[4,33],"high":[5,15,166],"reliability":[6,39,200],"and":[7,28,37,45,78,104,120,172,184,187],"thermal":[8,35,81,128,156,191],"conductivity":[9,36,82,129,157,192],"of":[10,49,83,94,107,112,126,130,133,151,158,175,189,193,201],"high-power":[11],"modules":[12],"operating":[13],"at":[14],"temperatures,":[16],"sintered":[17,50,84,113,134,160,176,194],"nano-silver":[18],"(Ag)":[19],"has":[20],"garnered":[21],"significant":[22],"attention":[23],"as":[24],"an":[25,87],"excellent":[26],"interconnect":[27],"heat":[29,46,99],"transfer":[30,100],"layer,":[31],"particularly":[32],"its":[34,59,65,98],"other":[38],"research.":[40],"Since":[41],"mechanical":[43],"behavior":[44],"conduction":[47],"capacity":[48],"Ag":[51],"is":[52],"generally":[53],"regulated":[54],"by":[55],"changes":[56],"in":[57],"temperature,":[58],"microstructure":[60,170],"will":[61],"change":[62],"accordingly,":[63],"affecting":[64],"performance.":[66],"In":[67],"this":[68],"study,":[69],"a":[70],"machine":[71],"learning":[72],"model":[73,105,150],"was":[74,137,163],"used":[75],"evaluate":[77],"predict":[79],"Ag,":[85],"providing":[86],"effective":[88],"method":[89,180],"analyze":[91],"influence":[93],"microstructural":[95],"characteristics":[96],"on":[97,145],"properties.":[101],"Image":[102],"processing":[103],"simulation":[106],"scanning":[108],"electron":[109],"microscopy":[110],"images":[111,132],"nano-Ag":[114,135,161],"nanostructures":[115,136],"were":[116],"performed":[117,138],"using":[118,168],"MATLAB":[119],"Ansys":[121],"software.":[122],"A":[123],"batch":[124],"calculation":[125],"2D":[131],"obtain":[140],"sufficient":[141],"data":[142],"sets.":[143],"Based":[144],"artificial":[147],"neural":[148],"network":[149],"Bayesian":[152],"optimization,":[153],"equivalent":[155],"different":[159],"microstructures":[162],"predicted":[164],"with":[165],"accuracy":[167],"image":[171],"characteristic":[173],"parameters":[174],"nano-Ag.":[177],"The":[178],"proposed":[179],"enables":[181],"rapid,":[182],"effective,":[183],"accurate":[185],"evaluation":[186],"prediction":[188],"nano-Ag,":[195],"contributing":[196],"significantly":[197],"power":[202],"modules.":[203]},"counts_by_year":[{"year":2026,"cited_by_count":3}],"updated_date":"2026-10-30T12:39:25.321706","created_date":"2025-02-07T00:00:00"}
