{"id":"https://openalex.org/W7214899673","doi":"https://doi.org/10.35517/jsh-aaup.v1.02.04","title":"Explainable Multi-Target Machine Learning Framework for Predicting Thermophysical Properties of Chemical Elements Using Periodic Table Descriptors","display_name":"Explainable Multi-Target Machine Learning Framework for Predicting Thermophysical Properties of Chemical Elements Using Periodic Table Descriptors","publication_year":2026,"publication_date":"2026-09-30","ids":{"openalex":"https://openalex.org/W7214899673","doi":"https://doi.org/10.35517/jsh-aaup.v1.02.04"},"language":"en","primary_location":{"id":"doi:10.35517/jsh-aaup.v1.02.04","doi":"https://doi.org/10.35517/jsh-aaup.v1.02.04","is_oa":true,"landing_page_url":"https://doi.org/10.35517/jsh-aaup.v1.02.04","pdf_url":"https://jsh-aaup.aaup.edu/index.php/jsh-aaup/article/download/86/47","source":{"id":"https://openalex.org/S7407056770","display_name":"AAUP Journal of STEM and Health Sciences","issn_l":"3135-0585","issn":["3135-0585","3135-0593"],"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":"AAUP Journal of STEM and Health Sciences","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://jsh-aaup.aaup.edu/index.php/jsh-aaup/article/download/86/47","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5083374453","display_name":"Feras Abujaber","orcid":"https://orcid.org/0000-0001-5596-1093","observed_orcids":["https://orcid.org/0000-0001-5596-1093"]},"institutions":[{"id":"https://openalex.org/I53620714","display_name":"Arab American University","ror":"https://ror.org/04jmsq731","country_code":"PS","type":"education","lineage":["https://openalex.org/I53620714"]}],"countries":["PS"],"is_corresponding":false,"raw_author_name":"Feras Abujaber","raw_affiliation_strings":["Arab American University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Arab American University","institution_ids":["https://openalex.org/I53620714"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000491635","display_name":"Faady M. Siouri","orcid":"https://orcid.org/0000-0001-9006-884X","observed_orcids":["https://orcid.org/0000-0001-9006-884X"]},"institutions":[{"id":"https://openalex.org/I53620714","display_name":"Arab American University","ror":"https://ror.org/04jmsq731","country_code":"PS","type":"education","lineage":["https://openalex.org/I53620714"]}],"countries":["PS"],"is_corresponding":false,"raw_author_name":"Faady Siouri","raw_affiliation_strings":["Department of Industrial Chemistry , Faculty of Sciences , Arab American University (AAUP) , P.O. Box 240 , Jenin , Palestine"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial Chemistry , Faculty of Sciences , Arab American University (AAUP) , P.O. Box 240 , Jenin , Palestine","institution_ids":["https://openalex.org/I53620714"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I53620714"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"1","issue":"2","first_page":"51","last_page":"64"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9990000128746033,"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":0.9990000128746033,"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/T13297","display_name":"History and advancements in chemistry","score":0.0008999999845400453,"subfield":{"id":"https://openalex.org/subfields/1207","display_name":"History and Philosophy of Science"},"field":{"id":"https://openalex.org/fields/12","display_name":"Arts and Humanities"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13645","display_name":"Thermodynamic and Structural Properties of Metals and Alloys","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/2506","display_name":"Metals and Alloys"},"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/melting-point-prediction","display_name":"melting point prediction","score":0.8579999804496765},{"id":"https://openalex.org/keywords/boiling-point-prediction","display_name":"boiling point prediction","score":0.9990000128746033},{"id":"https://openalex.org/keywords/atomic-number-density","display_name":"atomic density","score":0.75},{"id":"https://openalex.org/keywords/atomic-radius","display_name":"atomic radii","score":0.8899999856948853},{"id":"https://openalex.org/keywords/multi-target-regression","display_name":"multi-target regression","score":0.7689999938011169},{"id":"https://openalex.org/keywords/explainable-machine-learning","display_name":"explainable machine learning","score":0.9269999861717224},{"id":"https://openalex.org/keywords/extra-trees","display_name":"extra trees","score":0.7059999704360962},{"id":"https://openalex.org/keywords/shap","display_name":"SHAP","score":0.8450000286102295},{"id":"https://openalex.org/keywords/permutation-feature-importance","display_name":"permutation importance","score":0.8939999938011169},{"id":"https://openalex.org/keywords/ionization-potential","display_name":"ionization potential","score":0.9210000038146973},{"id":"https://openalex.org/keywords/relativistic-effects","display_name":"relativistic effects","score":0.75},{"id":"https://openalex.org/keywords/radioactive-elements","display_name":"radioactive elements","score":0.4320000112056732}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6046000123023987},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4982999861240387},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46299999952316284},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.41130000352859497},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.362199991941452},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3292999863624573},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.31540000438690186},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.31540000438690186},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.310699999332428},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2808000147342682},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2687999904155731}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.35517/jsh-aaup.v1.02.04","doi":"https://doi.org/10.35517/jsh-aaup.v1.02.04","is_oa":true,"landing_page_url":"https://doi.org/10.35517/jsh-aaup.v1.02.04","pdf_url":"https://jsh-aaup.aaup.edu/index.php/jsh-aaup/article/download/86/47","source":{"id":"https://openalex.org/S7407056770","display_name":"AAUP Journal of STEM and Health Sciences","issn_l":"3135-0585","issn":["3135-0585","3135-0593"],"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":"AAUP Journal of STEM and Health Sciences","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.35517/jsh-aaup.v1.02.04","doi":"https://doi.org/10.35517/jsh-aaup.v1.02.04","is_oa":true,"landing_page_url":"https://doi.org/10.35517/jsh-aaup.v1.02.04","pdf_url":"https://jsh-aaup.aaup.edu/index.php/jsh-aaup/article/download/86/47","source":{"id":"https://openalex.org/S7407056770","display_name":"AAUP Journal of STEM and Health Sciences","issn_l":"3135-0585","issn":["3135-0585","3135-0593"],"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":"AAUP Journal of STEM and Health Sciences","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7214899673.pdf","grobid_xml":"https://content.openalex.org/works/W7214899673.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W1678356000","https://openalex.org/W2011301426","https://openalex.org/W2056132907","https://openalex.org/W2074616700","https://openalex.org/W2097968133","https://openalex.org/W2104489082","https://openalex.org/W2112081648","https://openalex.org/W2115098571","https://openalex.org/W2122111042","https://openalex.org/W2230728100","https://openalex.org/W2282821441","https://openalex.org/W2295598076","https://openalex.org/W2342249984","https://openalex.org/W2464725281","https://openalex.org/W2790960441","https://openalex.org/W2884430236","https://openalex.org/W2911964244","https://openalex.org/W2912934387","https://openalex.org/W2963784900","https://openalex.org/W2968923792","https://openalex.org/W2999615587","https://openalex.org/W3035965352","https://openalex.org/W3128261063","https://openalex.org/W4234275475","https://openalex.org/W4234698323","https://openalex.org/W4239510810"],"related_works":["https://openalex.org/W4396518683","https://openalex.org/W2565212977","https://openalex.org/W2558166069","https://openalex.org/W7203598484","https://openalex.org/W2146905318","https://openalex.org/W4388117120","https://openalex.org/W4391691359","https://openalex.org/W7135197544","https://openalex.org/W7083631316","https://openalex.org/W7202298592"],"abstract_inverted_index":{"The":[0,135,231],"thermophysical":[1],"properties":[2],"of":[3,131,159,161,181],"the":[4,81,120,128,132,142,149,155,168,199,220,228],"chemical":[5],"elements":[6,238],"underpin":[7],"materials":[8],"selection,":[9],"process":[10],"design,":[11],"and":[12,47,70,90,108,165,174,192,207,216,246,248,255],"fundamental":[13],"chemistry,":[14],"yet":[15],"their":[16],"experimental":[17],"determination":[18,160],"remains":[19],"difficult":[20],"for":[21,80,127,167,202],"rare,":[22],"radioactive,":[23],"or":[24],"synthetic":[25],"species.":[26],"This":[27],"study":[28],"develops":[29],"an":[30],"explainable,":[31],"multi-target":[32],"machine":[33],"learning":[34],"framework":[35,150,156],"that":[36],"predicts":[37],"four":[38],"key":[39],"properties,":[40],"namely":[41],"melting":[42,169,203],"point,":[43,45,170,172,204,206],"boiling":[44,171,205],"density,":[46,173],"atomic":[48,59,61,175,223],"radius,":[49,176],"directly":[50],"from":[51,75],"readily":[52],"available":[53],"periodic-table":[54],"descriptors.":[55],"Twelve":[56],"descriptors,":[57],"including":[58],"number,":[60],"mass,":[62],"period,":[63],"group,":[64],"electronegativity,":[65],"ionization":[66],"energy,":[67],"electron":[68,72,76,217],"affinity,":[69],"valence-shell":[71],"counts":[73],"derived":[74],"configurations,":[77],"were":[78,93,235],"compiled":[79],"elements.":[82],"Eight":[83],"regression":[84],"algorithms":[85],"spanning":[86],"linear,":[87],"kernel,":[88],"instance-based,":[89],"tree-ensemble":[91],"families":[92],"systematically":[94],"benchmarked":[95],"using":[96,188],"repeated":[97],"five-fold":[98],"cross-validation,":[99],"with":[100,213,227,237],"all":[101],"targets":[102],"modeled":[103],"on":[104],"a":[105],"natural-logarithmic":[106],"scale":[107],"missing":[109],"descriptors":[110],"handled":[111],"by":[112,141],"leakage-free":[113],"iterative":[114],"imputation.":[115],"Tree-based":[116],"ensembles":[117],"considerably":[118],"outperformed":[119],"linear":[121],"models,":[122],"which":[123,146],"did":[124],"not":[125],"account":[126],"nonlinear":[129],"nature":[130],"periodic":[133],"relationships.":[134],"best":[136],"mean":[137],"performance":[138],"was":[139,186],"provided":[140],"Extra":[143],"Trees":[144],"algorithm,":[145],"served":[147],"as":[148,198,243,253],"base":[151],"learner.":[152],"After":[153],"optimization,":[154],"reached":[157],"coefficients":[158],"0.72,":[162],"0.76,":[163],"0.88,":[164],"0.74":[166],"respectively,":[177],"alongside":[178],"low":[179],"values":[180],"root-mean-square":[182],"error.":[183],"Model":[184],"behavior":[185],"interpreted":[187],"Shapley":[189],"additive":[190],"explanations":[191],"permutation":[193],"importance.":[194],"Ionization":[195],"energy":[196],"emerged":[197],"dominant":[200],"descriptor":[201],"density":[208],"owing":[209],"to":[210],"its":[211],"correlation":[212],"bonding":[214,241],"strength":[215],"localization,":[218],"whereas":[219],"period":[221],"governed":[222],"radius":[224],"in":[225],"line":[226],"shell":[229],"structure.":[230],"largest":[232],"prediction":[233],"errors":[234],"associated":[236],"exhibiting":[239],"anomalous":[240],"(such":[242,252],"carbon,":[244],"boron,":[245],"phosphorus)":[247],"strong":[249],"relativistic":[250],"effects":[251],"gold":[254],"mercury).":[256]},"counts_by_year":[],"updated_date":"2026-11-12T12:28:57.059296","created_date":"2026-10-01T00:00:00"}