{"id":"https://openalex.org/W3192149447","doi":"https://doi.org/10.1371/journal.pone.0255637","title":"Bandgap prediction of two-dimensional materials using machine learning","display_name":"Bandgap prediction of two-dimensional materials using machine learning","publication_year":2021,"publication_date":"2021-08-13","ids":{"openalex":"https://openalex.org/W3192149447","doi":"https://doi.org/10.1371/journal.pone.0255637","mag":"3192149447","pmid":"https://pubmed.ncbi.nlm.nih.gov/34388173","pmcid":"https://www.ncbi.nlm.nih.gov/pmc/articles/8363013"},"language":"en","primary_location":{"id":"doi:10.1371/journal.pone.0255637","doi":"https://doi.org/10.1371/journal.pone.0255637","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pone.0255637","pdf_url":"https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0255637&type=printable","source":{"id":"https://openalex.org/S202381698","display_name":"PLoS ONE","issn_l":"1932-6203","issn":["1932-6203"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","erih-plus","jufo-1","ki-jl-1","medline","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310315706","host_organization_name":"Public Library of Science","host_organization_lineage":["https://openalex.org/P4310315706"],"host_organization_lineage_names":["Public Library of Science"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLOS ONE","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0255637&type=printable","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100433746","display_name":"Yu Zhang","orcid":"https://orcid.org/0000-0003-4391-3371","observed_orcids":["https://orcid.org/0000-0003-4391-3371"]},"institutions":[{"id":"https://openalex.org/I202028630","display_name":"Changchun Normal University","ror":"https://ror.org/00cbhey71","country_code":"CN","type":"education","lineage":["https://openalex.org/I202028630"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yu Zhang","raw_affiliation_strings":["Department of Computer Science and Technology, Changchun Normal University, Changchun, China"],"raw_orcid":"https://orcid.org/0000-0003-4391-3371","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Changchun Normal University, Changchun, China","institution_ids":["https://openalex.org/I202028630"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007270980","display_name":"Wenjing Xu","orcid":"https://orcid.org/0000-0002-9817-4601","observed_orcids":["https://orcid.org/0000-0002-9817-4601","https://orcid.org/0009-0004-7860-6552"]},"institutions":[{"id":"https://openalex.org/I202028630","display_name":"Changchun Normal University","ror":"https://ror.org/00cbhey71","country_code":"CN","type":"education","lineage":["https://openalex.org/I202028630"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenjing Xu","raw_affiliation_strings":["Department of Computer Science and Technology, Changchun Normal University, Changchun, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Changchun Normal University, Changchun, China","institution_ids":["https://openalex.org/I202028630"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062243742","display_name":"Guangjie Liu","orcid":"https://orcid.org/0000-0003-4729-7406","observed_orcids":["https://orcid.org/0000-0003-4729-7406"]},"institutions":[{"id":"https://openalex.org/I202028630","display_name":"Changchun Normal University","ror":"https://ror.org/00cbhey71","country_code":"CN","type":"education","lineage":["https://openalex.org/I202028630"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Guangjie Liu","raw_affiliation_strings":["Department of Computer Science and Technology, Changchun Normal University, Changchun, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Changchun Normal University, Changchun, China","institution_ids":["https://openalex.org/I202028630"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103110315","display_name":"Zhiyong Zhang","orcid":"https://orcid.org/0000-0002-8782-2730","observed_orcids":["https://orcid.org/0000-0002-8782-2730"]},"institutions":[{"id":"https://openalex.org/I202028630","display_name":"Changchun Normal University","ror":"https://ror.org/00cbhey71","country_code":"CN","type":"education","lineage":["https://openalex.org/I202028630"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiyong Zhang","raw_affiliation_strings":["Department of Computer Science and Technology, Changchun Normal University, Changchun, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Changchun Normal University, Changchun, China","institution_ids":["https://openalex.org/I202028630"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061733763","display_name":"Jinlong Zhu","orcid":"https://orcid.org/0000-0002-5723-2879","observed_orcids":["https://orcid.org/0000-0002-5723-2879"]},"institutions":[{"id":"https://openalex.org/I202028630","display_name":"Changchun Normal University","ror":"https://ror.org/00cbhey71","country_code":"CN","type":"education","lineage":["https://openalex.org/I202028630"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinlong Zhu","raw_affiliation_strings":["Department of Computer Science and Technology, Changchun Normal University, Changchun, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Changchun Normal University, Changchun, China","institution_ids":["https://openalex.org/I202028630"]}]},{"author_position":"last","author":{"id":null,"display_name":"Meng Li","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I157507598","display_name":"Shenyang University of Technology","ror":"https://ror.org/00d7f8730","country_code":"CN","type":"education","lineage":["https://openalex.org/I157507598"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meng Li","raw_affiliation_strings":["College of Information Science and Engineering, Shenyang University of Technology, Shenyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Shenyang University of Technology, Shenyang, China","institution_ids":["https://openalex.org/I157507598"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5062243742","https://openalex.org/A5100433746"],"corresponding_institution_ids":["https://openalex.org/I202028630"],"apc_list":{"value":1749,"currency":"USD","value_usd":1749},"apc_paid":{"value":1749,"currency":"USD","value_usd":1749},"fwci":1.7796,"has_fulltext":true,"cited_by_count":53,"citation_normalized_percentile":{"value":0.8477136,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"16","issue":"8","first_page":"e0255637","last_page":"e0255637"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9994999766349792,"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":0.9994999766349792,"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/T10275","display_name":"2D Materials and Applications","score":0.00039999998989515007,"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/T10002","display_name":"Advanced Chemical Physics Studies","score":0.0,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/band-gap-prediction","display_name":"band gap prediction","score":0.9470000267028809},{"id":"https://openalex.org/keywords/2d-materials","display_name":"two-dimensional materials","score":1.0},{"id":"https://openalex.org/keywords/machine-learning","display_name":"machine learning","score":0.8309999704360962},{"id":"https://openalex.org/keywords/gradient-boosted-decision-trees","display_name":"gradient boosting decision tree","score":0.8529999852180481},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.9629999995231628},{"id":"https://openalex.org/keywords/support-vector-regression","display_name":"support vector regression","score":1.0},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"multilayer perceptron","score":0.9409999847412109},{"id":"https://openalex.org/keywords/spin-orbit-coupling","display_name":"spin-orbit coupling","score":0.9620000123977661},{"id":"https://openalex.org/keywords/mos2","display_name":"MoS2","score":0.875},{"id":"https://openalex.org/keywords/graphene","display_name":"graphene","score":0.9169999957084656}],"concepts":[{"id":"https://openalex.org/C181966813","wikidata":"https://www.wikidata.org/wiki/Q806352","display_name":"Band gap","level":2,"score":0.7758362293243408},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.5794445872306824},{"id":"https://openalex.org/C108225325","wikidata":"https://www.wikidata.org/wiki/Q11456","display_name":"Semiconductor","level":2,"score":0.521480917930603},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.4962018132209778},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4931430220603943},{"id":"https://openalex.org/C30080830","wikidata":"https://www.wikidata.org/wiki/Q169917","display_name":"Graphene","level":2,"score":0.43637996912002563},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4347987771034241},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4165133833885193},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3838052749633789},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36339735984802246},{"id":"https://openalex.org/C49040817","wikidata":"https://www.wikidata.org/wiki/Q193091","display_name":"Optoelectronics","level":1,"score":0.3564271926879883},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32903140783309937},{"id":"https://openalex.org/C171250308","wikidata":"https://www.wikidata.org/wiki/Q11468","display_name":"Nanotechnology","level":1,"score":0.2456279993057251},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.23889213800430298},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.192917138338089},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.12770283222198486}],"mesh":[{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001896","descriptor_name":"Boron Compounds","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D004220","descriptor_name":"Disulfides","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D004553","descriptor_name":"Electric Conductivity","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D006108","descriptor_name":"Graphite","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008982","descriptor_name":"Molybdenum","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012666","descriptor_name":"Semiconductors","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D015233","descriptor_name":"Models, Statistical","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D062126","descriptor_name":"Databases, Chemical","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":5,"locations":[{"id":"doi:10.1371/journal.pone.0255637","doi":"https://doi.org/10.1371/journal.pone.0255637","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pone.0255637","pdf_url":"https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0255637&type=printable","source":{"id":"https://openalex.org/S202381698","display_name":"PLoS ONE","issn_l":"1932-6203","issn":["1932-6203"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","erih-plus","jufo-1","ki-jl-1","medline","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310315706","host_organization_name":"Public Library of Science","host_organization_lineage":["https://openalex.org/P4310315706"],"host_organization_lineage_names":["Public Library of Science"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLOS ONE","raw_type":"journal-article"},{"id":"pmid:34388173","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/34388173","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,"listed_in":[],"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":"PloS one","raw_type":"Journal Article"},{"id":"pmh:oai:pubmedcentral.nih.gov:8363013","doi":null,"is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC8363013/","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLoS One","raw_type":"Text"},{"id":"pmh:oai:RePEc:plo:pone00:0255637","doi":null,"is_oa":false,"landing_page_url":"https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0255637","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},{"id":"pmh:oai:doaj.org/article:448941464d5948468b9f2f67607b6209","doi":null,"is_oa":true,"landing_page_url":"https://doaj.org/article/448941464d5948468b9f2f67607b6209","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"PLoS ONE, Vol 16, Iss 8, p e0255637 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1371/journal.pone.0255637","doi":"https://doi.org/10.1371/journal.pone.0255637","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pone.0255637","pdf_url":"https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0255637&type=printable","source":{"id":"https://openalex.org/S202381698","display_name":"PLoS ONE","issn_l":"1932-6203","issn":["1932-6203"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","erih-plus","jufo-1","ki-jl-1","medline","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310315706","host_organization_name":"Public Library of Science","host_organization_lineage":["https://openalex.org/P4310315706"],"host_organization_lineage_names":["Public Library of Science"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLOS ONE","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G3180924245","display_name":null,"funder_award_id":"61604019","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3638337682","display_name":null,"funder_award_id":"2020-KF-22-08","funder_id":"https://openalex.org/F4320326907","funder_display_name":"State Key Laboratory of Robotics"},{"id":"https://openalex.org/G8332284566","display_name":null,"funder_award_id":"2020-KF-22-08","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320326907","display_name":"State Key Laboratory of Robotics","ror":"https://ror.org/03pcw1780"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3192149447.pdf","grobid_xml":"https://content.openalex.org/works/W3192149447.grobid-xml"},"referenced_works_count":51,"referenced_works":["https://openalex.org/W1795630677","https://openalex.org/W2017871657","https://openalex.org/W2058122340","https://openalex.org/W2146096861","https://openalex.org/W2254450385","https://openalex.org/W2508390250","https://openalex.org/W2772378680","https://openalex.org/W2779016088","https://openalex.org/W2787882182","https://openalex.org/W2788484525","https://openalex.org/W2789478641","https://openalex.org/W2790939418","https://openalex.org/W2793991578","https://openalex.org/W2806275814","https://openalex.org/W2809779474","https://openalex.org/W2884430236","https://openalex.org/W2890313024","https://openalex.org/W2906922775","https://openalex.org/W2909378280","https://openalex.org/W2914794309","https://openalex.org/W2915603306","https://openalex.org/W2917193084","https://openalex.org/W2919418796","https://openalex.org/W2923537029","https://openalex.org/W2929345585","https://openalex.org/W2956468808","https://openalex.org/W2968923792","https://openalex.org/W2971819914","https://openalex.org/W2980888952","https://openalex.org/W2981825398","https://openalex.org/W2991261860","https://openalex.org/W2995011869","https://openalex.org/W3003859633","https://openalex.org/W3008349555","https://openalex.org/W3010382286","https://openalex.org/W3012558541","https://openalex.org/W3014677556","https://openalex.org/W3015565194","https://openalex.org/W3017266955","https://openalex.org/W3022561955","https://openalex.org/W3022916199","https://openalex.org/W3026514716","https://openalex.org/W3030239822","https://openalex.org/W3034093793","https://openalex.org/W3035322616","https://openalex.org/W3037752050","https://openalex.org/W3046171581","https://openalex.org/W3049657624","https://openalex.org/W3094112494","https://openalex.org/W3094150515","https://openalex.org/W3103819594"],"related_works":["https://openalex.org/W7127155383","https://openalex.org/W6922583620","https://openalex.org/W4392008768","https://openalex.org/W4410303206","https://openalex.org/W3119125442","https://openalex.org/W7136296624","https://openalex.org/W4382657591","https://openalex.org/W4414705387","https://openalex.org/W7167917952","https://openalex.org/W4385898138"],"abstract_inverted_index":{"The":[0,229],"bandgap":[1,36,50,198],"of":[2,21,26,37,67,77,105,116,131,155,166,188,211,231,243],"two-dimensional":[3],"(2D)":[4],"materials":[5,133,139,157,245],"plays":[6],"an":[7,159],"important":[8],"role":[9],"in":[10,64,152],"their":[11],"applications":[12],"to":[13,29,71,92,101,112,127,218],"various":[14],"devices.":[15,69],"For":[16],"instance,":[17],"the":[18,24,34,49,65,72,103,114,129,136,197,209,212,233,241],"gapless":[19],"nature":[20],"graphene":[22],"limits":[23],"use":[25],"this":[27,120],"material":[28,58],"semiconductor":[30,68],"device":[31],"applications,":[32],"whereas":[33],"indirect":[35],"molybdenum":[38],"disulfide":[39],"is":[40,184,204,235],"suitable":[41],"for":[42,54],"electrical":[43],"and":[44,52,146,162,169,178,191,225],"photo-device":[45],"applications.":[46],"Therefore,":[47],"predicting":[48,93,153],"rapidly":[51,248],"accurately":[53],"a":[55,89,207],"given":[56],"2D":[57,117,132,138,156,244],"structure":[59],"has":[60],"great":[61],"scientific":[62],"significance":[63],"manufacturing":[66],"Compared":[70],"extremely":[73],"high":[74,254],"computation":[75],"cost":[76],"conventional":[78],"first-principles":[79],"calculations,":[80],"machine":[81],"learning":[82],"(ML)":[83],"based":[84,134],"on":[85,135],"statistics":[86],"may":[87],"be":[88,247],"promising":[90],"alternative":[91],"bandgaps.":[94],"Although":[95],"ML":[96,125,214,251],"algorithms":[97,126],"have":[98,108],"been":[99,110],"used":[100,111,205],"predict":[102,113,128],"properties":[104,115,242],"materials,":[106],"they":[107],"rarely":[109],"materials.":[118],"In":[119],"study,":[121],"we":[122],"apply":[123],"four":[124,213],"bandgaps":[130,154],"computational":[137],"database":[140],"(C2DB).":[141],"Gradient":[142],"boosted":[143],"decision":[144],"trees":[145],"random":[147],"forests":[148],"are":[149],"more":[150],"effective":[151],"with":[158,186,253],"R2":[160,183,230],">90%":[161],"root-mean-square":[163],"error":[164],"(RMSE)":[165],"~0.24":[167],"eV":[168,190],"0.27":[170],"eV,":[171,193,220,222,224,227],"respectively.":[172,194,228],"By":[173],"contrast,":[174],"support":[175],"vector":[176],"regression":[177],"multi-layer":[179],"perceptron":[180],"show":[181,239],"that":[182,240],">70%":[185],"RMSE":[187],"~0.41":[189],"0.43":[192],"Finally,":[195],"when":[196],"calculated":[199],"without":[200],"spin-orbit":[201],"coupling":[202],"(SOC)":[203],"as":[206],"feature,":[208],"RMSEs":[210],"models":[215,234],"decrease":[216],"greatly":[217],"0.09":[219],"0.10":[221],"0.17":[223],"0.12":[226],"all":[232],">94%.":[236],"These":[237],"results":[238],"can":[246],"obtained":[249],"by":[250],"prediction":[252],"precision.":[255]},"counts_by_year":[{"year":2026,"cited_by_count":14},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":5}],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2021-08-16T00:00:00"}
