{"id":"https://openalex.org/W4379538682","doi":"https://doi.org/10.48550/arxiv.2306.02015","title":"Machine learning enabled experimental design and parameter estimation for ultrafast spin dynamics","display_name":"Machine learning enabled experimental design and parameter estimation for ultrafast spin dynamics","publication_year":2023,"publication_date":"2023-06-03","ids":{"openalex":"https://openalex.org/W4379538682","doi":"https://doi.org/10.48550/arxiv.2306.02015"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2306.02015","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2306.02015","pdf_url":"https://arxiv.org/pdf/2306.02015","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":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2306.02015","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034462680","display_name":"Zhantao Chen","orcid":"https://orcid.org/0000-0003-1954-3868"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Zhantao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101649163","display_name":"Peng Cheng","orcid":"https://orcid.org/0000-0002-9267-1789"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng, Cheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060478806","display_name":"Alexander N. Petsch","orcid":"https://orcid.org/0000-0002-7597-9626"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Petsch, Alexander N.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068743423","display_name":"Sathya R. Chitturi","orcid":"https://orcid.org/0000-0002-0298-8476"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chitturi, Sathya R.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082922139","display_name":"Alana Okullo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Okullo, Alana","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070895353","display_name":"Sugata Chowdhury","orcid":"https://orcid.org/0000-0001-5480-6075"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chowdhury, Sugata","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056060411","display_name":"Chun Hong Yoon","orcid":"https://orcid.org/0000-0003-1482-1662"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yoon, Chun Hong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5033321609","display_name":"Joshua J. Turner","orcid":"https://orcid.org/0000-0002-2106-7955"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Turner, Joshua J.","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":true,"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":0.9991000294685364,"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":0.9991000294685364,"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/T11177","display_name":"Spectroscopy and Quantum Chemical Studies","score":0.974399983882904,"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/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.9641000032424927,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6270787119865417},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5367138981819153},{"id":"https://openalex.org/keywords/spin","display_name":"Spin (aerodynamics)","score":0.4966736435890198},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4904274642467499},{"id":"https://openalex.org/keywords/experimental-data","display_name":"Experimental data","score":0.4610840976238251},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44800713658332825},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44747892022132874},{"id":"https://openalex.org/keywords/statistical-physics","display_name":"Statistical physics","score":0.3812456727027893},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.25896257162094116},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11303871870040894}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6270787119865417},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5367138981819153},{"id":"https://openalex.org/C42704618","wikidata":"https://www.wikidata.org/wiki/Q910917","display_name":"Spin (aerodynamics)","level":2,"score":0.4966736435890198},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4904274642467499},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.4610840976238251},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44800713658332825},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44747892022132874},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.3812456727027893},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.25896257162094116},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11303871870040894},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2306.02015","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2306.02015","pdf_url":"https://arxiv.org/pdf/2306.02015","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":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2306.02015","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2306.02015","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":"pmh:oai:arXiv.org:2306.02015","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2306.02015","pdf_url":"https://arxiv.org/pdf/2306.02015","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":"","raw_type":"text"},"sustainable_development_goals":[{"score":0.5099999904632568,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[{"id":"https://openalex.org/G1229526393","display_name":null,"funder_award_id":"AC02-76SF00515","funder_id":"https://openalex.org/F4320332359","funder_display_name":"Office of Science"},{"id":"https://openalex.org/G1286251239","display_name":null,"funder_award_id":"DE-AC02","funder_id":"https://openalex.org/F4320337480","funder_display_name":"Basic Energy Sciences"},{"id":"https://openalex.org/G2129191951","display_name":null,"funder_award_id":"DE-AC02-76SF00515","funder_id":"https://openalex.org/F4320332359","funder_display_name":"Office of Science"},{"id":"https://openalex.org/G2371548206","display_name":null,"funder_award_id":"76SF00515","funder_id":"https://openalex.org/F4320306084","funder_display_name":"U.S. Department of Energy"},{"id":"https://openalex.org/G2871405021","display_name":null,"funder_award_id":"DE-AC02-76SF00515","funder_id":"https://openalex.org/F4320337480","funder_display_name":"Basic Energy Sciences"},{"id":"https://openalex.org/G3748695461","display_name":null,"funder_award_id":"DE-AC02-76SF00515","funder_id":"https://openalex.org/F4320306084","funder_display_name":"U.S. Department of Energy"},{"id":"https://openalex.org/G4514663388","display_name":null,"funder_award_id":"DE-SC0022216","funder_id":"https://openalex.org/F4320306084","funder_display_name":"U.S. Department of Energy"},{"id":"https://openalex.org/G498139845","display_name":null,"funder_award_id":"DE-AC02","funder_id":"https://openalex.org/F4320332359","funder_display_name":"Office of Science"},{"id":"https://openalex.org/G6550344761","display_name":null,"funder_award_id":"76SF00515","funder_id":"https://openalex.org/F4320337480","funder_display_name":"Basic Energy Sciences"},{"id":"https://openalex.org/G6558272803","display_name":null,"funder_award_id":"DE-AC02","funder_id":"https://openalex.org/F4320306084","funder_display_name":"U.S. Department of Energy"},{"id":"https://openalex.org/G7283024673","display_name":null,"funder_award_id":"AC02-76SF00515","funder_id":"https://openalex.org/F4320337480","funder_display_name":"Basic Energy Sciences"},{"id":"https://openalex.org/G7490537531","display_name":null,"funder_award_id":"AC02-76SF00515","funder_id":"https://openalex.org/F4320306084","funder_display_name":"U.S. Department of Energy"},{"id":"https://openalex.org/G7702459498","display_name":null,"funder_award_id":"AC02-76SF00515","funder_id":"https://openalex.org/F4320338381","funder_display_name":"SLAC National Accelerator Laboratory"},{"id":"https://openalex.org/G8211782511","display_name":null,"funder_award_id":"DE-AC02-76SF00515","funder_id":"https://openalex.org/F4320338381","funder_display_name":"SLAC National Accelerator Laboratory"},{"id":"https://openalex.org/G8382000481","display_name":null,"funder_award_id":"76SF00515","funder_id":"https://openalex.org/F4320332359","funder_display_name":"Office of Science"},{"id":"https://openalex.org/G925106177","display_name":null,"funder_award_id":"DE-SC0022216","funder_id":"https://openalex.org/F4320337480","funder_display_name":"Basic Energy Sciences"},{"id":"https://openalex.org/G969889393","display_name":null,"funder_award_id":"DE-AC02-","funder_id":"https://openalex.org/F4320306084","funder_display_name":"U.S. Department of Energy"}],"funders":[{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"},{"id":"https://openalex.org/F4320332359","display_name":"Office of Science","ror":"https://ror.org/00mmn6b08"},{"id":"https://openalex.org/F4320337480","display_name":"Basic Energy Sciences","ror":"https://ror.org/05mg91w61"},{"id":"https://openalex.org/F4320338381","display_name":"SLAC National Accelerator Laboratory","ror":"https://ror.org/05gzmn429"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4379538682.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W2391251536","https://openalex.org/W2362198218","https://openalex.org/W1982750869","https://openalex.org/W2019521278","https://openalex.org/W1984922432","https://openalex.org/W2113077220","https://openalex.org/W2375008505","https://openalex.org/W2350679292","https://openalex.org/W2024147069"],"abstract_inverted_index":{"Advanced":[0],"experimental":[1,46,124],"measurements":[2,56,121],"are":[3],"crucial":[4],"for":[5,57,67,72,93],"driving":[6],"theoretical":[7],"developments":[8],"and":[9,16,28,75,96,117,130,146],"unveiling":[10],"novel":[11],"phenomena":[12],"in":[13,78,110],"condensed":[14],"matter":[15],"material":[17],"physics,":[18],"which":[19],"often":[20],"suffer":[21],"from":[22,85],"the":[23,33,86,104],"scarcity":[24],"of":[25,82,107],"facility":[26],"resources":[27],"increasing":[29],"complexities.":[30],"To":[31],"address":[32],"limitations,":[34],"we":[35],"introduce":[36],"a":[37,63],"methodology":[38],"that":[39],"combines":[40],"machine":[41],"learning":[42],"with":[43,50],"Bayesian":[44],"optimal":[45],"design":[47],"(BOED),":[48],"exemplified":[49],"x-ray":[51],"photon":[52],"fluctuation":[53],"spectroscopy":[54],"(XPFS)":[55],"spin":[58,69,131],"fluctuations.":[59],"Our":[60,100],"method":[61,109,134],"employs":[62],"neural":[64,87],"network":[65,88],"model":[66,89,115],"large-scale":[68],"dynamics":[70],"simulations":[71],"precise":[73],"distribution":[74],"utility":[76],"calculations":[77],"BOED.":[79],"The":[80],"capability":[81],"automatic":[83],"differentiation":[84],"is":[90],"further":[91],"leveraged":[92],"more":[94,119,142],"robust":[95],"accurate":[97],"parameter":[98],"estimation.":[99],"numerical":[101],"benchmarks":[102],"demonstrate":[103],"superior":[105],"performance":[106],"our":[108,133],"guiding":[111],"XPFS":[112,129],"experiments,":[113,140],"predicting":[114],"parameters,":[116],"yielding":[118],"informative":[120],"within":[122],"limited":[123],"time.":[125],"Although":[126],"focusing":[127],"on":[128],"fluctuations,":[132],"can":[135],"be":[136],"adapted":[137],"to":[138],"other":[139],"facilitating":[141],"efficient":[143],"data":[144],"collection":[145],"accelerating":[147],"scientific":[148],"discoveries.":[149]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}
