{"id":"https://openalex.org/W7163279396","doi":"https://doi.org/10.48550/arxiv.2606.00776","title":"Latent Diffusion Pretraining for Crystal Property Prediction","display_name":"Latent Diffusion Pretraining for Crystal Property Prediction","publication_year":2026,"publication_date":"2026-05-30","ids":{"openalex":"https://openalex.org/W7163279396","doi":"https://doi.org/10.48550/arxiv.2606.00776"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.00776","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.00776","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.2606.00776","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5040759397","display_name":"Shrimon Mukherjee","orcid":"https://orcid.org/0009-0008-3138-631X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mukherjee, Shrimon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137659085","display_name":"Kishalay Das","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Das, Kishalay","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060417636","display_name":"Partha Basuchowdhuri","orcid":"https://orcid.org/0000-0001-7655-7591"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Basuchowdhuri, Partha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137618153","display_name":"Pawan Goyal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Goyal, Pawan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5073812421","display_name":"Niloy Ganguly","orcid":"https://orcid.org/0000-0002-3967-186X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ganguly, Niloy","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/T11948","display_name":"Machine Learning in Materials Science","score":0.9932000041007996,"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":0.9932000041007996,"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/T10798","display_name":"Crystallography and molecular interactions","score":0.0007999999797903001,"subfield":{"id":"https://openalex.org/subfields/1606","display_name":"Physical and Theoretical Chemistry"},"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/T11825","display_name":"Catalysis and Oxidation Reactions","score":0.0005000000237487257,"subfield":{"id":"https://openalex.org/subfields/1503","display_name":"Catalysis"},"field":{"id":"https://openalex.org/fields/15","display_name":"Chemical Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6732000112533569},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.636900007724762},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6108999848365784},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.5637999773025513},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5077000260353088},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.48809999227523804},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.46299999952316284},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.4146000146865845},{"id":"https://openalex.org/keywords/crystal","display_name":"Crystal (programming language)","score":0.3937999904155731}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7207000255584717},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6732000112533569},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.636900007724762},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6108999848365784},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.5637999773025513},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5077000260353088},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5062999725341797},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.48809999227523804},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.46299999952316284},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4146000146865845},{"id":"https://openalex.org/C2781285689","wikidata":"https://www.wikidata.org/wiki/Q21921428","display_name":"Crystal (programming language)","level":2,"score":0.3937999904155731},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.38510000705718994},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.37599998712539673},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3709999918937683},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3684999942779541},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3458999991416931},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3425999879837036},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.3418000042438507},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.33739998936653137},{"id":"https://openalex.org/C68710425","wikidata":"https://www.wikidata.org/wiki/Q5275442","display_name":"Diffusion process","level":3,"score":0.3312999904155731},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3222000002861023},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.3199000060558319},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.30649998784065247},{"id":"https://openalex.org/C84947059","wikidata":"https://www.wikidata.org/wiki/Q5191417","display_name":"Crystal structure prediction","level":3,"score":0.3028999865055084},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2800999879837036},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.26600000262260437},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C186060115","wikidata":"https://www.wikidata.org/wiki/Q30336093","display_name":"Biological system","level":1,"score":0.25279998779296875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.00776","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.00776","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.2606.00776","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.00776","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Fast":[0],"and":[1,18,49,138,171,178,183,196],"accurate":[2],"prediction":[3,153,163],"of":[4,39,176],"crystal":[5,55,112],"properties":[6,56],"is":[7,124,211],"a":[8,42,79,95,100,115],"central":[9],"challenge":[10],"in":[11,50,70,193],"new":[12],"materials":[13],"design.":[14],"Graph":[15],"neural":[16],"networks":[17],"Transformer-based":[19],"models":[20,46],"have":[21,67],"emerged":[22],"as":[23],"powerful":[24],"tools":[25],"for":[26,54,150,161],"this":[27,75],"task":[28],"due":[29],"to":[30,33,88,134,200],"their":[31],"ability":[32],"encode":[34],"the":[35,104,121,131,181,188],"local":[36],"structural":[37,137],"environment":[38],"atoms":[40],"within":[41,119],"crystal.":[43],"However,":[44],"these":[45,72],"are":[47,57,197],"data-hungry,":[48],"practice,":[51],"labeled":[52],"data":[53,90],"scarce.":[58],"Pretraining-finetuning":[59],"strategies,":[60],"particularly":[61],"those":[62],"based":[63,83],"on":[64,157,180],"diffusion":[65,82,101,122,128],"models,":[66],"shown":[68],"promise":[69],"addressing":[71],"limitations.":[73],"In":[74],"work,":[76],"we":[77],"introduce":[78],"novel":[80],"latent":[81,117,127],"pretraining":[84,105,129],"framework,":[85],"CrysLDNet,":[86],"designed":[87],"mitigate":[89],"scarcity.":[91],"Our":[92],"approach":[93],"integrates":[94],"Variational":[96],"Autoencoder":[97],"(VAE)":[98],"with":[99,174,206],"model":[102],"during":[103],"stage.":[106],"The":[107],"VAE":[108],"encoder":[109,133],"maps":[110],"3D":[111],"structures":[113],"into":[114],"smooth":[116],"space":[118],"which":[120,145],"process":[123],"applied.":[125],"This":[126],"enables":[130],"graph":[132],"effectively":[135],"capture":[136],"chemical":[139],"semantics":[140],"from":[141],"large-scale":[142],"unlabeled":[143],"data,":[144],"can":[146],"then":[147],"be":[148],"finetuned":[149,205],"specific":[151],"property":[152,162],"tasks.":[154],"Comprehensive":[155],"experiments":[156],"popular":[158],"DFT":[159,202],"datasets":[160],"reveal":[164],"that":[165],"CrysLDNet":[166],"significantly":[167],"outperforms":[168],"both":[169],"training-from-scratch":[170],"pretrained":[172],"baselines,":[173],"improvements":[175],"4.26%":[177],"4.90%":[179],"JARVIS":[182],"MP":[184],"datasets,":[185],"respectively.":[186],"Additionally,":[187],"learned":[189],"representations":[190],"remain":[191],"robust":[192],"sparse-data":[194],"conditions":[195],"expressive":[198],"enough":[199],"correct":[201],"errors":[203],"when":[204],"limited":[207],"experimental":[208],"data.":[209],"Code":[210],"available":[212],"at:":[213],"https://github.com/shrimonmuke0202/CrysLDNet.git.":[214]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-03T00:00:00"}