{"id":"https://openalex.org/W7164852187","doi":"https://doi.org/10.48550/arxiv.2606.13955","title":"Smoothing Dark Areas in Molecular Latent Diffusion","display_name":"Smoothing Dark Areas in Molecular Latent Diffusion","publication_year":2026,"publication_date":"2026-06-11","ids":{"openalex":"https://openalex.org/W7164852187","doi":"https://doi.org/10.48550/arxiv.2606.13955"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.13955","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.13955","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.13955","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138637487","display_name":"Xi Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138646470","display_name":"Jiahan Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jiahan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138686105","display_name":"Yuxuan Xia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xia, Yuxuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100864921","display_name":"Yingcheng Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Yingcheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128248131","display_name":"Shaoyi Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Shaoyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138656213","display_name":"Shengjie Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Shengjie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.23019999265670776,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.23019999265670776,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11948","display_name":"Machine Learning in Materials Science","score":0.15109999477863312,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.12219999730587006,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.6018000245094299},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.48649999499320984},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4562000036239624},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.4505999982357025},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.44449999928474426},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.40290001034736633},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.3813999891281128}],"concepts":[{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.6018000245094299},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.554099977016449},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.48649999499320984},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4668000042438507},{"id":"https://openalex.org/C186060115","wikidata":"https://www.wikidata.org/wiki/Q30336093","display_name":"Biological system","level":1,"score":0.462799996137619},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4562000036239624},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.4505999982357025},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.44449999928474426},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.40290001034736633},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.39419999718666077},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3813999891281128},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.36739999055862427},{"id":"https://openalex.org/C99726746","wikidata":"https://www.wikidata.org/wiki/Q906396","display_name":"Chemical space","level":3,"score":0.32510000467300415},{"id":"https://openalex.org/C58024561","wikidata":"https://www.wikidata.org/wiki/Q207721","display_name":"Latent heat","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C59593255","wikidata":"https://www.wikidata.org/wiki/Q901663","display_name":"Molecular dynamics","level":2,"score":0.3001999855041504},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2962999939918518},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29170000553131104},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.2888999879360199},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.28529998660087585},{"id":"https://openalex.org/C68709404","wikidata":"https://www.wikidata.org/wiki/Q1134475","display_name":"Flux (metallurgy)","level":2,"score":0.28439998626708984}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.13955","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.13955","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.13955","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.13955","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Latent":[0],"diffusion":[1,60],"is":[2],"a":[3,14,41,95,129],"promising":[4],"framework":[5],"for":[6,116],"scalable":[7],"3D":[8],"molecular":[9,27,74],"generation,":[10,73],"but":[11,62],"it":[12],"requires":[13,76],"latent":[15,42,54,85],"space":[16,55],"that":[17,46,56,98],"remains":[18],"smooth,":[19],"valid,":[20],"and":[21,79,108,125,146,150],"navigable":[22],"beyond":[23],"posterior":[24],"samples.":[25],"Existing":[26],"VAEs,":[28],"however,":[29],"are":[30,57],"typically":[31],"learned":[32],"through":[33],"reconstruction-based":[34],"objectives,":[35],"which":[36],"do":[37],"not":[38],"guarantee":[39],"such":[40],"space.":[43],"We":[44,91],"show":[45],"this":[47],"leads":[48],"to":[49,64],"dark":[50,100],"areas:":[51],"regions":[52],"of":[53],"reachable":[58],"during":[59,111],"sampling":[61],"decode":[63],"disconnected":[65],"or":[66],"chemically":[67],"invalid":[68],"molecules.":[69],"Unlike":[70],"in":[71],"image":[72],"decoding":[75],"strict":[77],"structural":[78,107],"chemical":[80,109,118],"precision,":[81],"so":[82],"even":[83],"small":[84],"perturbations":[86],"can":[87],"produce":[88],"catastrophic":[89],"failures.":[90],"therefore":[92],"propose":[93],"TopVAE,":[94],"topology-optimized":[96],"VAE":[97],"reduces":[99],"areas":[101],"by":[102],"making":[103],"the":[104,114,138],"decoder":[105],"internalize":[106],"constraints":[110],"training,":[112],"eliminating":[113],"need":[115],"test-time":[117],"correction.":[119],"TopVAE":[120],"greatly":[121],"improves":[122],"off-posterior":[123],"robustness,":[124],"when":[126],"paired":[127],"with":[128],"standard":[130],"DiT,":[131],"achieves":[132],"$77\\%$":[133],"lower":[134,142],"FCD-3D":[135,143],"on":[136,144,153],"QM9,":[137],"highest":[139],"V&amp;C,":[140],"$52\\%$":[141],"GEOM-Drugs,":[145],"$1.29{\\times}$":[147],"more":[148],"stable":[149],"connected":[151],"molecules":[152],"zero-shot":[154],"scaffold":[155],"inpainting.":[156]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-16T00:00:00"}
