{"id":"https://openalex.org/W7147189662","doi":"https://doi.org/10.1109/cnml68938.2026.11452333","title":"A Variational Autoencoder Framework for Generating Molecular Structures from Raman Spectra of Vibrational Probes","display_name":"A Variational Autoencoder Framework for Generating Molecular Structures from Raman Spectra of Vibrational Probes","publication_year":2026,"publication_date":"2026-01-30","ids":{"openalex":"https://openalex.org/W7147189662","doi":"https://doi.org/10.1109/cnml68938.2026.11452333"},"language":null,"primary_location":{"id":"doi:10.1109/cnml68938.2026.11452333","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11452333","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","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/A5132717074","display_name":"Zhan Li","orcid":null},"institutions":[{"id":"https://openalex.org/I4210162190","display_name":"China University of Petroleum, East China","ror":"https://ror.org/05gbn2817","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162190"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhan Li","raw_affiliation_strings":["China University of Petroleum,School Of Materials Science And Engineering,Qingdao,China"],"affiliations":[{"raw_affiliation_string":"China University of Petroleum,School Of Materials Science And Engineering,Qingdao,China","institution_ids":["https://openalex.org/I4210162190"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5132592227","display_name":"Hao Ren","orcid":null},"institutions":[{"id":"https://openalex.org/I4210162190","display_name":"China University of Petroleum, East China","ror":"https://ror.org/05gbn2817","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162190"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Ren","raw_affiliation_strings":["China University of Petroleum,School Of Materials Science And Engineering,Qingdao,China"],"affiliations":[{"raw_affiliation_string":"China University of Petroleum,School Of Materials Science And Engineering,Qingdao,China","institution_ids":["https://openalex.org/I4210162190"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5132717074"],"corresponding_institution_ids":["https://openalex.org/I4210162190"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.90876628,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"953","last_page":"958"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.5461999773979187,"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":0.5461999773979187,"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/T12748","display_name":"Molecular spectroscopy and chirality","score":0.23280000686645508,"subfield":{"id":"https://openalex.org/subfields/1607","display_name":"Spectroscopy"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11324","display_name":"Spectroscopy Techniques in Biomedical and Chemical Research","score":0.08489999920129776,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7414000034332275},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.6348999738693237},{"id":"https://openalex.org/keywords/spectral-line","display_name":"Spectral line","score":0.5024999976158142},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4641000032424927},{"id":"https://openalex.org/keywords/raman-spectroscopy","display_name":"Raman spectroscopy","score":0.43970000743865967},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.415800005197525},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4092000126838684},{"id":"https://openalex.org/keywords/molecular-vibration","display_name":"Molecular vibration","score":0.4081999957561493},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39559999108314514}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7414000034332275},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.6348999738693237},{"id":"https://openalex.org/C4839761","wikidata":"https://www.wikidata.org/wiki/Q212111","display_name":"Spectral line","level":2,"score":0.5024999976158142},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4641000032424927},{"id":"https://openalex.org/C40003534","wikidata":"https://www.wikidata.org/wiki/Q862228","display_name":"Raman spectroscopy","level":2,"score":0.43970000743865967},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.424699991941452},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.415800005197525},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4092000126838684},{"id":"https://openalex.org/C24123453","wikidata":"https://www.wikidata.org/wiki/Q900121","display_name":"Molecular vibration","level":3,"score":0.4081999957561493},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39730000495910645},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39559999108314514},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.38690000772476196},{"id":"https://openalex.org/C32891209","wikidata":"https://www.wikidata.org/wiki/Q483666","display_name":"Spectroscopy","level":2,"score":0.36899998784065247},{"id":"https://openalex.org/C32909587","wikidata":"https://www.wikidata.org/wiki/Q11369","display_name":"Molecule","level":2,"score":0.3684000074863434},{"id":"https://openalex.org/C153642686","wikidata":"https://www.wikidata.org/wiki/Q70906","display_name":"Infrared spectroscopy","level":2,"score":0.35659998655319214},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.34630000591278076},{"id":"https://openalex.org/C186060115","wikidata":"https://www.wikidata.org/wiki/Q30336093","display_name":"Biological system","level":1,"score":0.34380000829696655},{"id":"https://openalex.org/C39539562","wikidata":"https://www.wikidata.org/wiki/Q7858699","display_name":"Two-dimensional infrared spectroscopy","level":3,"score":0.3310999870300293},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.33090001344680786},{"id":"https://openalex.org/C41999313","wikidata":"https://www.wikidata.org/wiki/Q489328","display_name":"Molecular physics","level":1,"score":0.3107999861240387},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29249998927116394},{"id":"https://openalex.org/C178910836","wikidata":"https://www.wikidata.org/wiki/Q2196961","display_name":"Molecular model","level":2,"score":0.28940001130104065},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.2854999899864197},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.2784999907016754},{"id":"https://openalex.org/C158355884","wikidata":"https://www.wikidata.org/wiki/Q11388","display_name":"Infrared","level":2,"score":0.2754000127315521},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cnml68938.2026.11452333","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11452333","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7080328464508057,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W4229007015","https://openalex.org/W4302425308","https://openalex.org/W4388991623","https://openalex.org/W4407958571","https://openalex.org/W4410340866","https://openalex.org/W4410340896","https://openalex.org/W4411068910","https://openalex.org/W4413777479","https://openalex.org/W7122627430"],"related_works":[],"abstract_inverted_index":{"Vibrational":[0],"probe":[1,18,40,97],"spectroscopy":[2],"is":[3,78,88,120],"an":[4,116,197],"effective":[5],"technique":[6],"for":[7,66,153],"investigating":[8],"complex":[9,31],"condensed-phase":[10],"systems.":[11,32],"Changes":[12],"in":[13,27,57],"the":[14,36,92,96,124,157,167,174,179,190],"vibrational":[15,43,82,127],"spectra":[16,44,128],"of":[17,30,39,53,95,147,178,206],"molecules":[19,41],"can":[20],"be":[21],"used":[22,89],"to":[23,90,122,188],"sensitively":[24],"monitor":[25],"variations":[26],"key":[28],"properties":[29],"However,":[33],"inversely":[34],"resolving":[35],"local":[37,101],"environment":[38],"from":[42,70],"remains":[45],"a":[46,81,133],"challenging":[47],"task.":[48],"Generative":[49],"models":[50],"are":[51,164,186],"capable":[52],"learning":[54],"latent":[55,93,112,130],"patterns":[56],"data":[58],"and":[59,99,106,111,129,150,159,203],"generating":[60],"new":[61],"structures,":[62],"showing":[63],"great":[64],"potential":[65],"inverse":[67],"structure":[68,154],"determination":[69],"spectroscopic":[71],"data.":[72],"In":[73,114],"this":[74],"work,":[75],"ethyl":[76],"acetate":[77],"employed":[79],"as":[80],"probe.":[83],"A":[84],"variational":[85],"autoencoder":[86],"(VAE)":[87],"learn":[91,123],"representation":[94],"molecule":[98],"its":[100],"chemical":[102],"environment,":[103],"enabling":[104],"encoding":[105],"decoding":[107],"between":[108,126],"molecular":[109,208],"structures":[110,209],"space.":[113],"addition,":[115],"artificial":[117],"neural":[118],"network":[119],"trained":[121],"relationship":[125],"variables,":[131],"forming":[132],"spectrum-to-latent-to-structure":[134],"spectrum\u2013structure":[135],"generative":[136,176],"model":[137,140],"(SSG).":[138],"The":[139,193],"achieves":[141],"root":[142],"mean":[143],"square":[144],"errors":[145],"(RMSEs)":[146],"0.058":[148],"\u00c5":[149,152],"0.311":[151],"prediction":[155],"on":[156],"training":[158],"test":[160],"sets,":[161],"respectively,":[162],"which":[163],"smaller":[165],"than":[166],"hydrogen":[168],"atomic":[169],"radius":[170],"(0.529":[171],"\u00c5),":[172],"demonstrating":[173],"strong":[175],"capability":[177],"model.":[180],"Furthermore,":[181],"discriminators":[182],"with":[183],"different":[184],"strengths":[185],"investigated":[187],"fine-tune":[189],"generated":[191],"structures.":[192],"results":[194],"show":[195],"that":[196],"appropriately":[198],"designed":[199],"discriminator":[200],"enables":[201],"fast":[202],"accurate":[204],"generation":[205],"correct":[207]},"counts_by_year":[],"updated_date":"2026-04-02T13:53:19.096889","created_date":"2026-04-02T00:00:00"}