{"id":"https://openalex.org/W7139966584","doi":"https://doi.org/10.48550/arxiv.2603.18650","title":"DeePAW: A universal machine learning model for orbital-free ab initio calculations","display_name":"DeePAW: A universal machine learning model for orbital-free ab initio calculations","publication_year":2026,"publication_date":"2026-03-19","ids":{"openalex":"https://openalex.org/W7139966584","doi":"https://doi.org/10.48550/arxiv.2603.18650"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.18650","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18650","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.18650","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130232302","display_name":"Tianhao Su","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Su, Tianhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050019770","display_name":"Shunbo Hu","orcid":"https://orcid.org/0000-0003-0472-0999"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Shunbo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130228891","display_name":"Yue Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Yue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130247253","display_name":"Runhai Oyang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Oyang, Runhai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130234984","display_name":"Xitao Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xitao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088149263","display_name":"Musen Li","orcid":"https://orcid.org/0000-0001-9844-8994"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Musen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079865740","display_name":"Jeffrey R. Reimers","orcid":"https://orcid.org/0000-0001-5157-7422"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Reimers, Jeffrey","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130250850","display_name":"Tong-Yi Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Tong-Yi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"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":0.9884999990463257,"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":0.9884999990463257,"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/T12557","display_name":"Inorganic Chemistry and Materials","score":0.002199999988079071,"subfield":{"id":"https://openalex.org/subfields/1604","display_name":"Inorganic 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/T12613","display_name":"X-ray Diffraction in Crystallography","score":0.0015999999595806003,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/ab-initio","display_name":"Ab initio","score":0.713100016117096},{"id":"https://openalex.org/keywords/density-functional-theory","display_name":"Density functional theory","score":0.46129998564720154},{"id":"https://openalex.org/keywords/electronic-structure","display_name":"Electronic structure","score":0.3578999936580658},{"id":"https://openalex.org/keywords/ab-initio-quantum-chemistry-methods","display_name":"Ab initio quantum chemistry methods","score":0.3472999930381775},{"id":"https://openalex.org/keywords/frontier","display_name":"Frontier","score":0.3447999954223633},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.34380000829696655},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3334999978542328}],"concepts":[{"id":"https://openalex.org/C2781442258","wikidata":"https://www.wikidata.org/wiki/Q46310","display_name":"Ab initio","level":2,"score":0.713100016117096},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6638000011444092},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5835999846458435},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5303999781608582},{"id":"https://openalex.org/C152365726","wikidata":"https://www.wikidata.org/wiki/Q1048589","display_name":"Density functional theory","level":2,"score":0.46129998564720154},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.4018000066280365},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3813999891281128},{"id":"https://openalex.org/C86025842","wikidata":"https://www.wikidata.org/wiki/Q5358432","display_name":"Electronic structure","level":2,"score":0.3578999936580658},{"id":"https://openalex.org/C183971685","wikidata":"https://www.wikidata.org/wiki/Q547605","display_name":"Ab initio quantum chemistry methods","level":3,"score":0.3472999930381775},{"id":"https://openalex.org/C2778571376","wikidata":"https://www.wikidata.org/wiki/Q1355821","display_name":"Frontier","level":2,"score":0.3447999954223633},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.34380000829696655},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3334999978542328},{"id":"https://openalex.org/C84947059","wikidata":"https://www.wikidata.org/wiki/Q5191417","display_name":"Crystal structure prediction","level":3,"score":0.3294000029563904},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C50292564","wikidata":"https://www.wikidata.org/wiki/Q2462783","display_name":"Computational learning theory","level":3,"score":0.2632000148296356},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.25850000977516174},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.25540000200271606}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.18650","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18650","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.18650","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18650","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.584069550037384}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Developing":[0],"universal":[1,36],"machine":[2,37],"learning":[3,38],"models":[4],"for":[5,41,131],"ab":[6,44],"initio":[7,45],"calculations":[8],"is":[9,34,55],"the":[10,18,27,49,57,64,69,76,87,104],"frontier":[11],"of":[12,21,72,99,121],"materials":[13,133],"cutting":[14],"edge":[15],"research":[16],"in":[17],"new":[19],"era":[20],"artificial":[22],"intelligence.":[23],"Here,":[24],"we":[25],"present":[26],"Deep":[28],"Augment":[29],"Way":[30],"model":[31,40,61],"(DeePAW)":[32],"that":[33],"a":[35],"(ML)":[39],"orbital-free":[42],"(OF)":[43],"calculations,":[46],"based":[47],"on":[48],"density":[50,115],"functional":[51],"theory":[52],"(DFT).":[53],"DeePAW":[54,100,117],"currently":[56],"best":[58],"OFDFT":[59],"ML":[60],"according":[62],"to":[63,80],"three":[65],"criterions,":[66],"1)":[67],"covering":[68],"largest":[70],"number":[71],"elements,":[73],"2)":[74],"having":[75],"widest":[77],"application":[78],"capability":[79],"diverse":[81],"crystal":[82],"structures,":[83],"and":[84,97,125],"3)":[85],"achieving":[86],"highest":[88],"prediction":[89],"accuracy":[90],"without":[91],"further":[92],"fine-tuning.":[93],"These":[94],"scientific":[95],"merits":[96],"innovations":[98],"are":[101],"stemmed":[102],"from":[103],"novel":[105],"SE(3)-equivariant":[106],"double":[107],"massage":[108],"passing":[109],"neuron":[110],"networks.":[111],"Besides":[112],"predicting":[113],"electron":[114],"distributions,":[116],"predicts":[118],"formation":[119],"energies":[120],"crystals":[122],"as":[123],"well":[124],"therefore":[126],"paves":[127],"an":[128],"efficient":[129],"avenue":[130],"multiscale":[132],"modeling":[134],"beyond":[135],"conventional":[136],"electronic":[137],"structure":[138],"calculation":[139],"methods.":[140]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-21T00:00:00"}