{"id":"https://openalex.org/W7157050614","doi":"https://doi.org/10.48550/arxiv.2604.24563","title":"Enhancing molecular dynamics with equivariant machine-learned densities","display_name":"Enhancing molecular dynamics with equivariant machine-learned densities","publication_year":2026,"publication_date":"2026-04-27","ids":{"openalex":"https://openalex.org/W7157050614","doi":"https://doi.org/10.48550/arxiv.2604.24563"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.24563","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.24563","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.24563","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061366692","display_name":"Mihail Bogojeski","orcid":"https://orcid.org/0000-0002-1839-7320"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bogojeski, Mihail","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089459531","display_name":"Muhammad R. Hasyim","orcid":"https://orcid.org/0000-0003-2056-9942"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hasyim, Muhammad R.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024209749","display_name":"Leslie Vogt-Maranto","orcid":"https://orcid.org/0000-0002-7006-4582"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vogt-Maranto, Leslie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134810516","display_name":"Klaus-Robert M\u00fcller","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"M\u00fcller, Klaus-Robert","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082307304","display_name":"Kieron Burke","orcid":"https://orcid.org/0000-0002-6159-0054"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Burke, Kieron","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5068215051","display_name":"Mark E. Tuckerman","orcid":"https://orcid.org/0000-0003-2194-9955"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tuckerman, Mark E.","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":0.9958999752998352,"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":0.9958999752998352,"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/T11804","display_name":"Quantum many-body systems","score":0.0005000000237487257,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.00039999998989515007,"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/observable","display_name":"Observable","score":0.6951000094413757},{"id":"https://openalex.org/keywords/dipole","display_name":"Dipole","score":0.5716000199317932},{"id":"https://openalex.org/keywords/molecular-dynamics","display_name":"Molecular dynamics","score":0.5544999837875366},{"id":"https://openalex.org/keywords/electronic-structure","display_name":"Electronic structure","score":0.5339999794960022},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5324000120162964},{"id":"https://openalex.org/keywords/non-equilibrium-thermodynamics","display_name":"Non-equilibrium thermodynamics","score":0.46380001306533813},{"id":"https://openalex.org/keywords/ab-initio","display_name":"Ab initio","score":0.4092000126838684},{"id":"https://openalex.org/keywords/spectral-line","display_name":"Spectral line","score":0.40119999647140503}],"concepts":[{"id":"https://openalex.org/C32848918","wikidata":"https://www.wikidata.org/wiki/Q845789","display_name":"Observable","level":2,"score":0.6951000094413757},{"id":"https://openalex.org/C173523689","wikidata":"https://www.wikidata.org/wiki/Q215589","display_name":"Dipole","level":2,"score":0.5716000199317932},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.5670999884605408},{"id":"https://openalex.org/C59593255","wikidata":"https://www.wikidata.org/wiki/Q901663","display_name":"Molecular dynamics","level":2,"score":0.5544999837875366},{"id":"https://openalex.org/C86025842","wikidata":"https://www.wikidata.org/wiki/Q5358432","display_name":"Electronic structure","level":2,"score":0.5339999794960022},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5324000120162964},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.48539999127388},{"id":"https://openalex.org/C74859849","wikidata":"https://www.wikidata.org/wiki/Q2466686","display_name":"Non-equilibrium thermodynamics","level":2,"score":0.46380001306533813},{"id":"https://openalex.org/C2781442258","wikidata":"https://www.wikidata.org/wiki/Q46310","display_name":"Ab initio","level":2,"score":0.4092000126838684},{"id":"https://openalex.org/C4839761","wikidata":"https://www.wikidata.org/wiki/Q212111","display_name":"Spectral line","level":2,"score":0.40119999647140503},{"id":"https://openalex.org/C41999313","wikidata":"https://www.wikidata.org/wiki/Q489328","display_name":"Molecular physics","level":1,"score":0.3725000023841858},{"id":"https://openalex.org/C152365726","wikidata":"https://www.wikidata.org/wiki/Q1048589","display_name":"Density functional theory","level":2,"score":0.3668999969959259},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.3598000109195709},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.3571000099182129},{"id":"https://openalex.org/C147120987","wikidata":"https://www.wikidata.org/wiki/Q2225","display_name":"Electron","level":2,"score":0.3197000026702881},{"id":"https://openalex.org/C92915299","wikidata":"https://www.wikidata.org/wiki/Q905186","display_name":"Electronic density","level":3,"score":0.30469998717308044},{"id":"https://openalex.org/C147597530","wikidata":"https://www.wikidata.org/wiki/Q369472","display_name":"Computational chemistry","level":1,"score":0.304500013589859},{"id":"https://openalex.org/C183971685","wikidata":"https://www.wikidata.org/wiki/Q547605","display_name":"Ab initio quantum chemistry methods","level":3,"score":0.2989000082015991},{"id":"https://openalex.org/C125485243","wikidata":"https://www.wikidata.org/wiki/Q905186","display_name":"Electron density","level":3,"score":0.29429998993873596},{"id":"https://openalex.org/C21074235","wikidata":"https://www.wikidata.org/wiki/Q3537681","display_name":"Molecular electronic transition","level":3,"score":0.29179999232292175},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2883000075817108},{"id":"https://openalex.org/C158355884","wikidata":"https://www.wikidata.org/wiki/Q11388","display_name":"Infrared","level":2,"score":0.2825999855995178},{"id":"https://openalex.org/C22693506","wikidata":"https://www.wikidata.org/wiki/Q3075290","display_name":"Hybrid functional","level":3,"score":0.2728999853134155},{"id":"https://openalex.org/C35052450","wikidata":"https://www.wikidata.org/wiki/Q2733663","display_name":"Electronic correlation","level":3,"score":0.26409998536109924},{"id":"https://openalex.org/C184779094","wikidata":"https://www.wikidata.org/wiki/Q26383","display_name":"Atomic physics","level":1,"score":0.2603999972343445},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.25859999656677246},{"id":"https://openalex.org/C19329426","wikidata":"https://www.wikidata.org/wiki/Q3537671","display_name":"Atomic electron transition","level":3,"score":0.2581999897956848}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.24563","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.24563","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.24563","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.24563","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":"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":{"Machine-learning":[0],"interatomic":[1],"potentials":[2],"(MLIPs)":[3],"have":[4],"enabled":[5],"molecular":[6,106,193],"dynamics":[7,107],"at":[8],"near":[9],"ab":[10],"initio":[11],"accuracy,":[12],"yet":[13],"remain":[14],"limited":[15],"to":[16,38,50,62,85,97,145,149,183],"energies":[17],"and":[18,29,108,117,143,188],"forces":[19],"by":[20],"construction,":[21],"leaving":[22],"electronic":[23,40,109,189],"observables":[24,190],"such":[25],"as":[26,82,174],"dipole":[27],"moments":[28],"polarizabilities":[30],"inaccessible.":[31],"We":[32,111],"introduce":[33],"DenSNet,":[34],"a":[35,67,74,83,102,180],"density-first":[36],"approach":[37,56],"machine-learned":[39,123],"structure":[41],"that":[42,77,169],"learns":[43],"the":[44,51,94,98,171,175],"Hohenberg--Kohn":[45],"map":[46],"from":[47,122],"nuclear":[48],"configurations":[49],"ground-state":[52],"electron":[53,172],"density.":[54],"Our":[55],"employs":[57],"an":[58],"SE(3)-equivariant":[59],"neural":[60],"network":[61,91],"predict":[63],"density":[64,96,162,173],"coefficients":[65],"of":[66,147,186],"flexible":[68],"atom-centered":[69],"Gaussian":[70],"basis,":[71],"combined":[72],"with":[73,128,140,160],"$\u0394$-learning":[75],"strategy":[76],"uses":[78],"superposed":[79],"atomic":[80],"densities":[81],"prior":[84],"accelerate":[86],"training.":[87],"A":[88],"second":[89],"equivariant":[90],"then":[92],"maps":[93],"predicted":[95],"total":[99],"energy,":[100],"providing":[101],"unified":[103],"framework":[104],"for":[105],"structure.":[110],"validate":[112],"DenSNet":[113],"on":[114,137],"ethanol,":[115],"ethanethiol,":[116],"resorcinol,":[118],"where":[119],"infrared":[120,157],"spectra":[121,158],"trajectories":[124,155],"show":[125,168],"excellent":[126],"agreement":[127],"experimental":[129],"gas-phase":[130],"measurements.":[131],"To":[132],"test":[133],"scalability,":[134],"we":[135,167],"train":[136],"polythiophene":[138],"oligomers":[139],"1--6":[141],"monomers":[142],"extrapolate":[144],"chains":[146],"up":[148],"12":[150],"monomers,":[151],"generating":[152],"stable":[153],"long-time":[154],"whose":[156],"agree":[159],"reference":[161],"functional":[163],"theory":[164],"calculations.":[165],"Here,":[166],"reinstating":[170],"central":[176],"learned":[177],"quantity":[178],"opens":[179],"practical":[181],"route":[182],"transferable":[184],"prediction":[185],"spectroscopic":[187],"in":[191],"large-scale":[192],"simulations.":[194]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-29T00:00:00"}