{"id":"https://openalex.org/W7162089574","doi":"https://doi.org/10.48550/arxiv.2605.21722","title":"MetaDNS: Enhancing Exploration in Discrete Neural Samplers via Well-Tempered Metadynamics","display_name":"MetaDNS: Enhancing Exploration in Discrete Neural Samplers via Well-Tempered Metadynamics","publication_year":2026,"publication_date":"2026-05-20","ids":{"openalex":"https://openalex.org/W7162089574","doi":"https://doi.org/10.48550/arxiv.2605.21722"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.21722","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21722","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.2605.21722","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136730075","display_name":"Xiaochen Du","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Du, Xiaochen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136797745","display_name":"Juno Nam","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nam, Juno","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136812821","display_name":"Jaemoo Choi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choi, Jaemoo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136782181","display_name":"Wei Guo","orcid":"https://orcid.org/0009-0009-5088-4784"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020498475","display_name":"Sathya Edamadaka","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Edamadaka, Sathya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136789875","display_name":"Junyi Sha","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sha, Junyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054657227","display_name":"Elton Pan","orcid":"https://orcid.org/0000-0003-0629-8429"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pan, Elton","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136812229","display_name":"Yongxin Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yongxin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136793891","display_name":"Molei Tao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tao, Molei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136751002","display_name":"Rafael G\u00f3mez-Bombarelli","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"G\u00f3mez-Bombarelli, Rafael","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/T11804","display_name":"Quantum many-body systems","score":0.6187999844551086,"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"}},"topics":[{"id":"https://openalex.org/T11804","display_name":"Quantum many-body systems","score":0.6187999844551086,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.24639999866485596,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.03370000049471855,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/metadynamics","display_name":"Metadynamics","score":0.9176999926567078},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5828999876976013},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.4968999922275543},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.4803999960422516},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4189999997615814},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.3869999945163727},{"id":"https://openalex.org/keywords/mode","display_name":"Mode (computer interface)","score":0.38199999928474426}],"concepts":[{"id":"https://openalex.org/C62752575","wikidata":"https://www.wikidata.org/wiki/Q6822487","display_name":"Metadynamics","level":3,"score":0.9176999926567078},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5828999876976013},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.4968999922275543},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.4803999960422516},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4620000123977661},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.42669999599456787},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4189999997615814},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.3869999945163727},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.38199999928474426},{"id":"https://openalex.org/C186060115","wikidata":"https://www.wikidata.org/wiki/Q30336093","display_name":"Biological system","level":1,"score":0.37599998712539673},{"id":"https://openalex.org/C44280652","wikidata":"https://www.wikidata.org/wiki/Q104837","display_name":"Phase (matter)","level":2,"score":0.367900013923645},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3407000005245209},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.3314000070095062},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3206000030040741},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.31790000200271606},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.3176000118255615},{"id":"https://openalex.org/C64297162","wikidata":"https://www.wikidata.org/wiki/Q1987070","display_name":"Deposition (geology)","level":3,"score":0.3165999948978424},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26429998874664307},{"id":"https://openalex.org/C47822265","wikidata":"https://www.wikidata.org/wiki/Q854457","display_name":"Complex system","level":2,"score":0.2621000111103058}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.21722","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21722","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.2605.21722","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21722","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":[{"id":"https://metadata.un.org/sdg/7","score":0.8772630095481873,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Sampling":[0],"from":[1,25],"discrete":[2,19,62],"distributions":[3],"with":[4,90],"multiple":[5],"modes":[6],"and":[7,15,28,44,108],"energy":[8,42,87],"barriers":[9],"is":[10,38],"fundamental":[11],"to":[12,30,95,119],"machine":[13],"learning":[14],"computational":[16],"physics.":[17],"Recent":[18],"neural":[20,92],"samplers":[21,93],"like":[22],"MDNS":[23],"suffer":[24],"mode":[26],"collapse":[27],"fail":[29],"sample":[31],"high-energy":[32,99],"barrier":[33],"regions":[34],"between":[35],"modes,":[36],"which":[37],"critical":[39],"for":[40],"free":[41,86],"estimation":[43],"understanding":[45],"phase":[46],"transitions.":[47],"We":[48],"propose":[49],"Metadynamics":[50],"Discrete":[51],"Neural":[52],"Sampler":[53],"(MetaDNS),":[54],"a":[55,96],"general":[56],"framework":[57],"integrating":[58],"well-tempered":[59],"metadynamics":[60],"into":[61],"diffusion":[63],"or":[64],"autoregressive":[65],"samplers.":[66],"By":[67],"maintaining":[68],"an":[69],"adaptive,":[70],"history-dependent":[71],"bias":[72,129],"potential":[73],"along":[74],"selected":[75],"low-dimensional":[76],"coordinates,":[77],"MetaDNS":[78,113,122],"forces":[79],"exploration":[80,126],"of":[81,98],"previously":[82],"inaccessible":[83],"regions,":[84],"enabling":[85],"reconstruction":[88],"infeasible":[89],"standard":[91],"due":[94],"lack":[97],"samples.":[100],"On":[101],"challenging":[102],"low-temperature":[103],"benchmarks":[104],"including":[105],"Ising,":[106],"Potts,":[107],"the":[109,115],"copper-gold":[110],"binary":[111],"alloy,":[112],"reproduces":[114],"thermodynamic":[116],"distribution.":[117],"Compared":[118],"MCMC-based":[120],"metadynamics,":[121],"also":[123],"achieves":[124],"comparable":[125],"requiring":[127],"fewer":[128],"deposition":[130],"steps.":[131]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-23T00:00:00"}