{"id":"https://openalex.org/W4385488649","doi":"https://doi.org/10.1109/ijcnn54540.2023.10191086","title":"Pre-Activation based Representation Learning to Enhance Predictive Analytics on Small Materials Data","display_name":"Pre-Activation based Representation Learning to Enhance Predictive Analytics on Small Materials Data","publication_year":2023,"publication_date":"2023-06-18","ids":{"openalex":"https://openalex.org/W4385488649","doi":"https://doi.org/10.1109/ijcnn54540.2023.10191086"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn54540.2023.10191086","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn54540.2023.10191086","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","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/A5035699206","display_name":"Vishu Gupta","orcid":"https://orcid.org/0000-0002-4931-7194"},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Vishu Gupta","raw_affiliation_strings":["Northwestern University,ECE Department,Evanston,Illinois,USA","ECE Department, Northwestern University, Evanston, Illinois, USA"],"affiliations":[{"raw_affiliation_string":"Northwestern University,ECE Department,Evanston,Illinois,USA","institution_ids":["https://openalex.org/I111979921"]},{"raw_affiliation_string":"ECE Department, Northwestern University, Evanston, Illinois, USA","institution_ids":["https://openalex.org/I111979921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047602285","display_name":"Wei\u2010keng Liao","orcid":"https://orcid.org/0009-0008-9411-2543"},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei-keng Liao","raw_affiliation_strings":["Northwestern University,ECE Department,Evanston,Illinois,USA","ECE Department, Northwestern University, Evanston, Illinois, USA"],"affiliations":[{"raw_affiliation_string":"Northwestern University,ECE Department,Evanston,Illinois,USA","institution_ids":["https://openalex.org/I111979921"]},{"raw_affiliation_string":"ECE Department, Northwestern University, Evanston, Illinois, USA","institution_ids":["https://openalex.org/I111979921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074976770","display_name":"Alok Choudhary","orcid":"https://orcid.org/0000-0001-8152-6319"},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alok Choudhary","raw_affiliation_strings":["Northwestern University,ECE Department,Evanston,Illinois,USA","ECE Department, Northwestern University, Evanston, Illinois, USA"],"affiliations":[{"raw_affiliation_string":"Northwestern University,ECE Department,Evanston,Illinois,USA","institution_ids":["https://openalex.org/I111979921"]},{"raw_affiliation_string":"ECE Department, Northwestern University, Evanston, Illinois, USA","institution_ids":["https://openalex.org/I111979921"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004659592","display_name":"Ankit Agrawal","orcid":"https://orcid.org/0000-0002-5519-0302"},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ankit Agrawal","raw_affiliation_strings":["Northwestern University,ECE Department,Evanston,Illinois,USA","ECE Department, Northwestern University, Evanston, Illinois, USA"],"affiliations":[{"raw_affiliation_string":"Northwestern University,ECE Department,Evanston,Illinois,USA","institution_ids":["https://openalex.org/I111979921"]},{"raw_affiliation_string":"ECE Department, Northwestern University, Evanston, Illinois, USA","institution_ids":["https://openalex.org/I111979921"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5035699206"],"corresponding_institution_ids":["https://openalex.org/I111979921"],"apc_list":null,"apc_paid":null,"fwci":0.7678,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.65724584,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":1.0,"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":1.0,"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/T12613","display_name":"X-ray Diffraction in Crystallography","score":0.9843000173568726,"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/T10736","display_name":"Hydrogen embrittlement and corrosion behaviors in metals","score":0.9799000024795532,"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/computer-science","display_name":"Computer science","score":0.757216215133667},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6675080060958862},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6270782947540283},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5856189131736755},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5720670819282532},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.42490527033805847},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4248628616333008},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.42162856459617615},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.404735803604126},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.15186193585395813}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.757216215133667},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6675080060958862},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6270782947540283},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5856189131736755},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5720670819282532},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.42490527033805847},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4248628616333008},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.42162856459617615},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.404735803604126},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.15186193585395813},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn54540.2023.10191086","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn54540.2023.10191086","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1562824993","https://openalex.org/W1565746575","https://openalex.org/W1800437104","https://openalex.org/W1865667476","https://openalex.org/W2018591442","https://openalex.org/W2033206800","https://openalex.org/W2112364454","https://openalex.org/W2112865076","https://openalex.org/W2123306226","https://openalex.org/W2143061492","https://openalex.org/W2164524421","https://openalex.org/W2278970271","https://openalex.org/W2338402873","https://openalex.org/W2464725281","https://openalex.org/W2490901606","https://openalex.org/W2559612475","https://openalex.org/W2565212977","https://openalex.org/W2804431384","https://openalex.org/W2810731239","https://openalex.org/W2902452488","https://openalex.org/W2953053221","https://openalex.org/W2963784900","https://openalex.org/W2963807552","https://openalex.org/W2978707075","https://openalex.org/W2990015413","https://openalex.org/W3022530152","https://openalex.org/W3039596418","https://openalex.org/W3100221827","https://openalex.org/W3100710928","https://openalex.org/W3105321997","https://openalex.org/W3113009196","https://openalex.org/W3121591497","https://openalex.org/W3129366682","https://openalex.org/W3129702700","https://openalex.org/W3208687975","https://openalex.org/W3213348597","https://openalex.org/W4212825158","https://openalex.org/W4225450288","https://openalex.org/W4242977197","https://openalex.org/W4285083923","https://openalex.org/W4293857795","https://openalex.org/W4360991008"],"related_works":["https://openalex.org/W2062195135","https://openalex.org/W2795079307","https://openalex.org/W2961085424","https://openalex.org/W2391251536","https://openalex.org/W2793058541","https://openalex.org/W2362198218","https://openalex.org/W1983629434","https://openalex.org/W1982750869","https://openalex.org/W2019521278","https://openalex.org/W1984922432"],"abstract_inverted_index":{"Artificial":[0],"intelligence":[1],"based":[2,96],"predictive":[3],"modeling":[4],"has":[5],"become":[6],"increasingly":[7],"sought-after":[8],"in":[9,273],"the":[10,110,125,130,222,229,232,237,265,270,274],"field":[11],"of":[12,129,231,276],"materials":[13,32,131],"science":[14],"for":[15,82,116,195,213,244],"training":[16,60,115,198,216],"property":[17],"prediction":[18],"models":[19,135,143,166,239,287],"due":[20],"to":[21,25,71,108,140,236,267],"their":[22],"promising":[23],"ability":[24],"extract":[26],"and":[27,58,67,80,147,160,199,206,217,240,248,284],"utilize":[28],"data-driven":[29],"information":[30,47],"from":[31,44,99,168],"data.":[33],"However,":[34],"current":[35],"methods":[36,225],"typically":[37],"use":[38,84],"limited":[39],"hand-engineered":[40],"fixed-length":[41],"representations":[42,97],"obtained":[43],"available":[45],"composition-based":[46,121],"only,":[48],"making":[49],"model":[50,101,114,197,215,233,279],"inputs":[51],"a":[52,69,90,100,104,259],"stumbling":[53],"block":[54],"when":[55],"handling":[56],"small":[57,145,200],"specialized":[59],"datasets.":[61],"In":[62],"this":[63],"paper,":[64],"we":[65,219],"study":[66],"propose":[68],"method":[70],"perform":[72,113,258],"representation":[73],"learning":[74,155],"(RL)":[75],"that":[76,93,221,269],"is":[77,288],"both":[78],"applicable":[79],"adaptive":[81],"generalized":[83],"across":[85],"various":[86],"domains.":[87],"We":[88,112,256],"introduce":[89],"RL":[91,224,278],"technique":[92],"utilizes":[94],"pre-activation":[95],"extracted":[98],"pre-trained":[102],"using":[103,120,251],"deep":[105,157],"neural":[106,158,163],"network":[107,159,164],"maximize":[109],"accuracy.":[111],"inorganic":[117],"material":[118],"properties":[119,249],"numerical":[122],"vectors":[123],"representing":[124],"elemental":[126],"fractions":[127],"(EF)":[128],"by":[132,250,263],"leveraging":[133],"source":[134,196],"trained":[136,167],"on":[137,144],"large":[138,189],"datasets":[139,146,194,212],"build":[141],"target":[142,214],"then":[148],"compare":[149],"its":[150],"performance":[151],"against":[152],"traditional":[153],"machine":[154],"(ML),":[156],"RL-based":[161,282],"graph":[162],"(GNN)":[165],"scratch":[169],"(SC)":[170],"with":[171],"EF":[172,253],"as":[173,180,182,184,234,254],"input,":[174,181],"more":[175],"informative":[176],"physical":[177],"attributes":[178],"(PA)":[179],"well":[183],"conventional":[185,241,285],"TL/RL":[186,242,286],"techniques.":[187],"Using":[188],"<tex":[190,202,208],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[191,203,209],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$(\\sim":[192,204,210],"345K)$</tex>":[193],"computational":[201],"28K)$</tex>":[205],"experimental":[207],"2K)$</tex>":[211],"testing,":[218],"show":[220],"proposed":[223,277],"help":[226],"significantly":[227],"improve":[228],"accuracy":[230,275],"compared":[235],"SC":[238],"techniques":[243],"all":[245],"data":[246],"sizes":[247],"only":[252],"input.":[255],"also":[257],"statistical":[260],"significance":[261],"analysis":[262],"calculating":[264],"p-value":[266],"find":[268],"observed":[271],"improvement":[272],"over":[280],"SC,":[281],"GNN,":[283],"indeed":[289],"significant.":[290]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2}],"updated_date":"2026-03-25T14:56:36.534964","created_date":"2025-10-10T00:00:00"}
