{"id":"https://openalex.org/W4318148013","doi":"https://doi.org/10.1109/bigdata55660.2022.10020289","title":"Conditional Graph Regression for Complex Chemical Systems with Heterogeneous Substructures","display_name":"Conditional Graph Regression for Complex Chemical Systems with Heterogeneous Substructures","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4318148013","doi":"https://doi.org/10.1109/bigdata55660.2022.10020289"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10020289","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata55660.2022.10020289","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"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/A5043362073","display_name":"Gyoung S. Na","orcid":"https://orcid.org/0000-0001-9803-0782"},"institutions":[{"id":"https://openalex.org/I4210151417","display_name":"Korea Research Institute of Chemical Technology","ror":"https://ror.org/043k4kk20","country_code":"KR","type":"facility","lineage":["https://openalex.org/I4210151417"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Gyoung S. Na","raw_affiliation_strings":["Korea Research Institute of Chemical Technology,Republic of Korea","Korea Research Institute of Chemical Technology, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Korea Research Institute of Chemical Technology,Republic of Korea","institution_ids":["https://openalex.org/I4210151417"]},{"raw_affiliation_string":"Korea Research Institute of Chemical Technology, Republic of Korea","institution_ids":["https://openalex.org/I4210151417"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5043362073"],"corresponding_institution_ids":["https://openalex.org/I4210151417"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.37152741,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"4","issue":null,"first_page":"1651","last_page":"1660"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9965000152587891,"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/T10409","display_name":"Fuel Cells and Related Materials","score":0.9861999750137329,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5920659899711609},{"id":"https://openalex.org/keywords/chemical-space","display_name":"Chemical space","score":0.4876631498336792},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.45581287145614624},{"id":"https://openalex.org/keywords/chemical-process","display_name":"Chemical process","score":0.45399364829063416},{"id":"https://openalex.org/keywords/physical-system","display_name":"Physical system","score":0.450241357088089},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4121227562427521},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32603752613067627},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.16636398434638977},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.08065196871757507}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5920659899711609},{"id":"https://openalex.org/C99726746","wikidata":"https://www.wikidata.org/wiki/Q906396","display_name":"Chemical space","level":3,"score":0.4876631498336792},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.45581287145614624},{"id":"https://openalex.org/C124223222","wikidata":"https://www.wikidata.org/wiki/Q2281940","display_name":"Chemical process","level":2,"score":0.45399364829063416},{"id":"https://openalex.org/C116672817","wikidata":"https://www.wikidata.org/wiki/Q1454986","display_name":"Physical system","level":2,"score":0.450241357088089},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4121227562427521},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32603752613067627},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.16636398434638977},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.08065196871757507},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C74187038","wikidata":"https://www.wikidata.org/wiki/Q1418791","display_name":"Drug discovery","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10020289","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata55660.2022.10020289","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322101","display_name":"Korea Research Institute of Chemical Technology","ror":"https://ror.org/043k4kk20"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W1992985800","https://openalex.org/W2112307556","https://openalex.org/W2141939342","https://openalex.org/W2161826494","https://openalex.org/W2606202972","https://openalex.org/W2606780347","https://openalex.org/W2612591418","https://openalex.org/W2766856748","https://openalex.org/W2897145571","https://openalex.org/W2908510526","https://openalex.org/W2937307539","https://openalex.org/W2947147416","https://openalex.org/W2949095042","https://openalex.org/W2961429249","https://openalex.org/W2962711740","https://openalex.org/W2963919031","https://openalex.org/W2964015378","https://openalex.org/W2965857891","https://openalex.org/W2969378264","https://openalex.org/W2976102057","https://openalex.org/W2978707075","https://openalex.org/W2990015413","https://openalex.org/W2998374989","https://openalex.org/W3010488723","https://openalex.org/W3012871709","https://openalex.org/W3033232773","https://openalex.org/W3037740742","https://openalex.org/W3092198360","https://openalex.org/W3093914769","https://openalex.org/W3099179075","https://openalex.org/W3109834426","https://openalex.org/W3113009196","https://openalex.org/W3113103820","https://openalex.org/W3125542198","https://openalex.org/W3128539297","https://openalex.org/W3131843789","https://openalex.org/W3137476274","https://openalex.org/W3138554248","https://openalex.org/W3163993681","https://openalex.org/W3174048174","https://openalex.org/W3187966659","https://openalex.org/W3189645387","https://openalex.org/W3190432377","https://openalex.org/W3201013727","https://openalex.org/W4282930045","https://openalex.org/W4287828570","https://openalex.org/W4297733535","https://openalex.org/W6726873649","https://openalex.org/W6736685754","https://openalex.org/W6737322904","https://openalex.org/W6754929296","https://openalex.org/W6757817989","https://openalex.org/W6762658119","https://openalex.org/W6784546583","https://openalex.org/W6841143912"],"related_works":["https://openalex.org/W2902857455","https://openalex.org/W2161782568","https://openalex.org/W4212982662","https://openalex.org/W4226062292","https://openalex.org/W4281772408","https://openalex.org/W4254684673","https://openalex.org/W2023175890","https://openalex.org/W1849940345","https://openalex.org/W4384825947","https://openalex.org/W3119100018"],"abstract_inverted_index":{"Graph":[0],"neural":[1],"networks":[2],"(GNNs)":[3],"have":[4],"been":[5],"widely":[6],"studied":[7],"as":[8,32,86],"an":[9],"efficient":[10],"and":[11,18,34,47,110,127,148,157],"generalized":[12],"method":[13],"to":[14],"predict":[15],"the":[16,64,71,80,108,114,120,124,131,146,152],"physical":[17,109,147],"chemical":[19,22,39,58,84,111,116,132,138,149],"properties":[20,112,150],"of":[21,44,113,151],"systems":[23,40,85,117],"based":[24],"on":[25,83,90],"a":[26,87],"single":[27],"homogeneous":[28,61],"atomic":[29,52,101,121],"structure,":[30],"such":[31],"molecule":[33],"crystalline":[35],"material.":[36],"However,":[37],"most":[38],"in":[41,70,130,144],"real-world":[42,136],"applications":[43,59],"materials":[45,156],"science":[46],"engineering":[48],"contain":[49],"multiple":[50],"heterogeneous":[51,125],"substructures.":[53,96],"Nonetheless,":[54],"existing":[55],"GNNs":[56,143],"for":[57],"assumed":[60],"graphs":[62],"with":[63],"input":[65,115],"node":[66],"or":[67],"edge":[68],"features":[69],"same":[72],"feature":[73],"space.":[74],"In":[75],"this":[76],"paper,":[77],"we":[78,98],"reformulate":[79],"regression":[81,88],"problem":[82,89],"core":[91,126],"substructures":[92,129],"conditioned":[93],"by":[94,118],"environment":[95,128],"Then,":[97],"propose":[99],"conditional":[100],"subgraph":[102],"interaction":[103],"network":[104],"(CASIN)":[105],"that":[106],"predicts":[107],"learning":[119],"interactions":[122],"between":[123],"systems.":[133,159],"For":[134],"three":[135],"benchmark":[137],"datasets,":[139],"CASIN":[140],"outperformed":[141],"state-of-the-art":[142],"predicting":[145],"complex":[153],"solar":[154],"cell":[155],"catalyst":[158]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}