{"id":"https://openalex.org/W4415330521","doi":"https://doi.org/10.48550/arxiv.2507.19438","title":"Gradient-based grand canonical optimization enabled by graph neural networks with fractional atomic existence","display_name":"Gradient-based grand canonical optimization enabled by graph neural networks with fractional atomic existence","publication_year":2025,"publication_date":"2025-07-25","ids":{"openalex":"https://openalex.org/W4415330521","doi":"https://doi.org/10.48550/arxiv.2507.19438"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2507.19438","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.19438","pdf_url":"https://arxiv.org/pdf/2507.19438","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2507.19438","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5055714128","display_name":"Mads-Peter Verner Christiansen","orcid":"https://orcid.org/0000-0002-3550-8379"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Christiansen, Mads-Peter Verner","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5081864343","display_name":"Bj\u00f8rk Hammer","orcid":"https://orcid.org/0000-0002-7849-6347"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hammer, Bj\u00f8rk","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/T10320","display_name":"Neural Networks and Applications","score":0.9222000241279602,"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"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9222000241279602,"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/formalism","display_name":"Formalism (music)","score":0.6075999736785889},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5910000205039978},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4997999966144562},{"id":"https://openalex.org/keywords/cartesian-coordinate-system","display_name":"Cartesian coordinate system","score":0.44339999556541443},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.37299999594688416},{"id":"https://openalex.org/keywords/interatomic-potential","display_name":"Interatomic potential","score":0.3619999885559082},{"id":"https://openalex.org/keywords/canonical-coordinates","display_name":"Canonical coordinates","score":0.33889999985694885},{"id":"https://openalex.org/keywords/surface","display_name":"Surface (topology)","score":0.32089999318122864}],"concepts":[{"id":"https://openalex.org/C73301696","wikidata":"https://www.wikidata.org/wiki/Q5469984","display_name":"Formalism (music)","level":3,"score":0.6075999736785889},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5910000205039978},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5195000171661377},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4997999966144562},{"id":"https://openalex.org/C16038011","wikidata":"https://www.wikidata.org/wiki/Q62912","display_name":"Cartesian coordinate system","level":2,"score":0.44339999556541443},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.37299999594688416},{"id":"https://openalex.org/C2776372370","wikidata":"https://www.wikidata.org/wiki/Q3399989","display_name":"Interatomic potential","level":3,"score":0.3619999885559082},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3499999940395355},{"id":"https://openalex.org/C3379466","wikidata":"https://www.wikidata.org/wiki/Q1501876","display_name":"Canonical coordinates","level":3,"score":0.33889999985694885},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3314000070095062},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.32089999318122864},{"id":"https://openalex.org/C854659","wikidata":"https://www.wikidata.org/wiki/Q1859284","display_name":"Message passing","level":2,"score":0.3066999912261963},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3041999936103821},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.29330000281333923},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C3020065929","wikidata":"https://www.wikidata.org/wiki/Q11379","display_name":"Total energy","level":3,"score":0.2651999890804291},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2619999945163727},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.26190000772476196},{"id":"https://openalex.org/C28556851","wikidata":"https://www.wikidata.org/wiki/Q1077753","display_name":"Canonical ensemble","level":3,"score":0.2506999969482422}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2507.19438","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.19438","pdf_url":"https://arxiv.org/pdf/2507.19438","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2507.19438","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2507.19438","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":"pmh:oai:arXiv.org:2507.19438","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.19438","pdf_url":"https://arxiv.org/pdf/2507.19438","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"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":[0],"learning":[1],"interatomic":[2],"potentials":[3],"have":[4],"become":[5],"an":[6],"indispensable":[7],"tool":[8],"for":[9,41,62,104],"materials":[10],"science,":[11],"enabling":[12],"the":[13,49,54,71,74,82],"study":[14],"of":[15,56,73,85],"larger":[16],"systems":[17],"and":[18,87,100],"longer":[19],"timescales.":[20],"State-of-the-art":[21],"models":[22],"are":[23,38],"generally":[24],"graph":[25],"neural":[26],"networks":[27],"that":[28,37,60],"employ":[29],"message":[30,50],"passing":[31,51],"to":[32,69,80],"iteratively":[33],"update":[34],"atomic":[35,64],"embeddings":[36],"ultimately":[39],"used":[40],"predicting":[42],"properties.":[43],"In":[44],"this":[45,91],"work":[46],"we":[47,92],"extend":[48],"formalism":[52],"with":[53,78],"inclusion":[55],"a":[57,94,105],"continuous":[58],"variable":[59],"accounts":[61],"fractional":[63],"existence.":[65,89],"This":[66],"allows":[67],"us":[68],"calculate":[70],"gradient":[72],"Gibbs":[75],"free":[76],"energy":[77],"respect":[79],"both":[81],"Cartesian":[83],"coordinates":[84],"atoms":[86],"their":[88],"Using":[90],"propose":[93],"gradient-based":[95],"grand":[96],"canonical":[97],"optimization":[98],"method":[99],"document":[101],"its":[102],"capabilities":[103],"Cu(110)":[106],"surface":[107],"oxide.":[108]},"counts_by_year":[],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2025-10-19T00:00:00"}
