{"id":"https://openalex.org/W4387899421","doi":"https://doi.org/10.1088/2632-2153/ad0652","title":"Neural network Gaussian processes as efficient models of potential energy surfaces for polyatomic molecules","display_name":"Neural network Gaussian processes as efficient models of potential energy surfaces for polyatomic molecules","publication_year":2023,"publication_date":"2023-10-24","ids":{"openalex":"https://openalex.org/W4387899421","doi":"https://doi.org/10.1088/2632-2153/ad0652"},"language":"en","primary_location":{"id":"doi:10.1088/2632-2153/ad0652","is_oa":true,"landing_page_url":"https://doi.org/10.1088/2632-2153/ad0652","pdf_url":"https://iopscience.iop.org/article/10.1088/2632-2153/ad0652/pdf","source":{"id":"https://openalex.org/S4210200687","display_name":"Machine Learning Science and Technology","issn_l":"2632-2153","issn":["2632-2153"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning: Science and Technology","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://iopscience.iop.org/article/10.1088/2632-2153/ad0652/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008029440","display_name":"Jun Dai","orcid":"https://orcid.org/0000-0002-2732-7316"},"institutions":[{"id":"https://openalex.org/I141945490","display_name":"University of British Columbia","ror":"https://ror.org/03rmrcq20","country_code":"CA","type":"education","lineage":["https://openalex.org/I141945490"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"J Dai","raw_affiliation_strings":["Department of Chemistry, The University of British Columbia, 2036 Main Mall, Vancouver, British Columbia, V6T 1Z1, CANADA"],"affiliations":[{"raw_affiliation_string":"Department of Chemistry, The University of British Columbia, 2036 Main Mall, Vancouver, British Columbia, V6T 1Z1, CANADA","institution_ids":["https://openalex.org/I141945490"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087205640","display_name":"Roman V. Krems","orcid":"https://orcid.org/0000-0002-9918-211X"},"institutions":[{"id":"https://openalex.org/I141945490","display_name":"University of British Columbia","ror":"https://ror.org/03rmrcq20","country_code":"CA","type":"education","lineage":["https://openalex.org/I141945490"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"R V Krems","raw_affiliation_strings":["Department of Chemistry, The University of British Columbia, 2036 Main Mall, British Columbia, V6T 1Z1, Vancouver, British Columbia, V6T 1Z4, CANADA"],"affiliations":[{"raw_affiliation_string":"Department of Chemistry, The University of British Columbia, 2036 Main Mall, British Columbia, V6T 1Z1, Vancouver, British Columbia, V6T 1Z4, CANADA","institution_ids":["https://openalex.org/I141945490"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5087205640"],"corresponding_institution_ids":["https://openalex.org/I141945490"],"apc_list":{"value":1600,"currency":"GBP","value_usd":1962},"apc_paid":{"value":1600,"currency":"GBP","value_usd":1962},"fwci":0.6864,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.63707385,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"4","issue":"4","first_page":"045027","last_page":"045027"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9993000030517578,"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":0.9993000030517578,"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/T10836","display_name":"Metabolomics and Mass Spectrometry Studies","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9797999858856201,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/gaussian-process","display_name":"Gaussian process","score":0.5924257636070251},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5869693756103516},{"id":"https://openalex.org/keywords/polyatomic-ion","display_name":"Polyatomic ion","score":0.5343785285949707},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5152001976966858},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.47959673404693604},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4465649425983429},{"id":"https://openalex.org/keywords/gaussian-function","display_name":"Gaussian function","score":0.43465158343315125},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4337608218193054},{"id":"https://openalex.org/keywords/kriging","display_name":"Kriging","score":0.4285678267478943},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4114045798778534},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4057416617870331},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3741227984428406},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3475619852542877},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3474581837654114},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3456451892852783},{"id":"https://openalex.org/keywords/statistical-physics","display_name":"Statistical physics","score":0.33239996433258057},{"id":"https://openalex.org/keywords/computational-chemistry","display_name":"Computational chemistry","score":0.18736883997917175},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.18025022745132446},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.12091401219367981},{"id":"https://openalex.org/keywords/molecule","display_name":"Molecule","score":0.09742999076843262},{"id":"https://openalex.org/keywords/quantum-mechanics","display_name":"Quantum mechanics","score":0.08142626285552979}],"concepts":[{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5924257636070251},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5869693756103516},{"id":"https://openalex.org/C66594872","wikidata":"https://www.wikidata.org/wiki/Q1064242","display_name":"Polyatomic ion","level":3,"score":0.5343785285949707},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5152001976966858},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.47959673404693604},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4465649425983429},{"id":"https://openalex.org/C7218915","wikidata":"https://www.wikidata.org/wiki/Q1054475","display_name":"Gaussian function","level":3,"score":0.43465158343315125},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4337608218193054},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.4285678267478943},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4114045798778534},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4057416617870331},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3741227984428406},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3475619852542877},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3474581837654114},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3456451892852783},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.33239996433258057},{"id":"https://openalex.org/C147597530","wikidata":"https://www.wikidata.org/wiki/Q369472","display_name":"Computational chemistry","level":1,"score":0.18736883997917175},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.18025022745132446},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.12091401219367981},{"id":"https://openalex.org/C32909587","wikidata":"https://www.wikidata.org/wiki/Q11369","display_name":"Molecule","level":2,"score":0.09742999076843262},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.08142626285552979},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1088/2632-2153/ad0652","is_oa":true,"landing_page_url":"https://doi.org/10.1088/2632-2153/ad0652","pdf_url":"https://iopscience.iop.org/article/10.1088/2632-2153/ad0652/pdf","source":{"id":"https://openalex.org/S4210200687","display_name":"Machine Learning Science and Technology","issn_l":"2632-2153","issn":["2632-2153"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning: Science and Technology","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:048dd7d9119e4d48ac5ea2cc98611611","is_oa":true,"landing_page_url":"https://doaj.org/article/048dd7d9119e4d48ac5ea2cc98611611","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning: Science and Technology, Vol 4, Iss 4, p 045027 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1088/2632-2153/ad0652","is_oa":true,"landing_page_url":"https://doi.org/10.1088/2632-2153/ad0652","pdf_url":"https://iopscience.iop.org/article/10.1088/2632-2153/ad0652/pdf","source":{"id":"https://openalex.org/S4210200687","display_name":"Machine Learning Science and Technology","issn_l":"2632-2153","issn":["2632-2153"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning: Science and Technology","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.8999999761581421}],"awards":[],"funders":[{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4387899421.pdf"},"referenced_works_count":78,"referenced_works":["https://openalex.org/W1567512734","https://openalex.org/W1969373275","https://openalex.org/W1987398410","https://openalex.org/W1995907282","https://openalex.org/W1998860313","https://openalex.org/W2009621017","https://openalex.org/W2024018448","https://openalex.org/W2025444507","https://openalex.org/W2055526416","https://openalex.org/W2058370262","https://openalex.org/W2063007245","https://openalex.org/W2067050455","https://openalex.org/W2071267590","https://openalex.org/W2073181637","https://openalex.org/W2084276247","https://openalex.org/W2130283669","https://openalex.org/W2135556320","https://openalex.org/W2142959074","https://openalex.org/W2164711568","https://openalex.org/W2168175751","https://openalex.org/W2241066782","https://openalex.org/W2526518998","https://openalex.org/W2527189750","https://openalex.org/W2527658566","https://openalex.org/W2547447472","https://openalex.org/W2585152223","https://openalex.org/W2595516356","https://openalex.org/W2725390203","https://openalex.org/W2727229501","https://openalex.org/W2743560992","https://openalex.org/W2766678531","https://openalex.org/W2775447200","https://openalex.org/W2781951421","https://openalex.org/W2788652239","https://openalex.org/W2790441086","https://openalex.org/W2791090109","https://openalex.org/W2792351009","https://openalex.org/W2793186536","https://openalex.org/W2805705780","https://openalex.org/W2885059312","https://openalex.org/W2897870668","https://openalex.org/W2904141086","https://openalex.org/W2922075711","https://openalex.org/W2923693308","https://openalex.org/W2924125158","https://openalex.org/W2932948170","https://openalex.org/W2950277916","https://openalex.org/W2963275575","https://openalex.org/W2993805018","https://openalex.org/W3001801734","https://openalex.org/W3016216085","https://openalex.org/W3086356742","https://openalex.org/W3088728644","https://openalex.org/W3096003755","https://openalex.org/W3099906879","https://openalex.org/W3103345637","https://openalex.org/W3104040444","https://openalex.org/W3104465572","https://openalex.org/W3106310231","https://openalex.org/W3162003518","https://openalex.org/W3201895347","https://openalex.org/W3207159821","https://openalex.org/W3212195610","https://openalex.org/W4226048270","https://openalex.org/W4287902508","https://openalex.org/W4294205985","https://openalex.org/W4315706285","https://openalex.org/W4360845352","https://openalex.org/W4361011198","https://openalex.org/W6679226052","https://openalex.org/W6681609451","https://openalex.org/W6745256532","https://openalex.org/W6753275298","https://openalex.org/W6758352740","https://openalex.org/W6771396984","https://openalex.org/W6773142731","https://openalex.org/W6842469858","https://openalex.org/W6848299440"],"related_works":["https://openalex.org/W566010457","https://openalex.org/W2600092203","https://openalex.org/W4300066510","https://openalex.org/W2056958800","https://openalex.org/W2803685231","https://openalex.org/W4293503520","https://openalex.org/W3134152097","https://openalex.org/W4311388919","https://openalex.org/W2966696655","https://openalex.org/W2384408398"],"abstract_inverted_index":{"Abstract":[0],"Kernel":[1],"models":[2,122,128,145,173,196,232,245,248,257,265],"of":[3,18,30,44,88,91,107,120,135,146,165,177,186,210,263],"potential":[4],"energy":[5,178,208],"surfaces":[6],"(PESs)":[7],"for":[8,129,138,266],"polyatomic":[9,139],"molecules":[10],"are":[11],"often":[12],"restricted":[13],"by":[14,26,62,175,204,241],"a":[15,95,98,104,117,236,260],"specific":[16,163],"choice":[17,262],"the":[19,28,31,41,45,52,63,68,133,166,200,206,211,216,222],"kernel":[20,32,70,167,264],"function.":[21,33,168],"This":[22],"can":[23,48,148,197],"be":[24,49,149,259],"avoided":[25],"optimizing":[27],"complexity":[29,90],"For":[34],"regression":[35],"problems":[36],"with":[37,103,123,235,249],"very":[38],"expensive":[39],"data,":[40],"functional":[42,238],"form":[43,164],"model":[46,213],"kernels":[47,93,125,228,234],"optimized":[50],"in":[51,199,215,221],"Gaussian":[53],"process":[54],"(GP)":[55],"setting":[56],"through":[57],"compositional":[58,69],"function":[59],"search":[60,71],"guided":[61],"Bayesian":[64,99],"information":[65],"criterion.":[66],"However,":[67],"is":[72],"computationally":[73],"demanding":[74],"and":[75,126,154,219],"relies":[76],"on":[77,161],"greedy":[78],"strategies,":[79],"which":[80,110],"may":[81],"yield":[82,155,229],"sub-optimal":[83],"kernels.":[84],"An":[85],"alternative":[86],"strategy":[87],"increasing":[89],"GP":[92,96,121,247],"treats":[94],"as":[97],"neural":[100],"network":[101],"(NN)":[102],"variable":[105,202],"number":[106],"hidden":[108],"layers,":[109],"yields":[111],"NNGP":[112,127,144,172,195,244,256],"models.":[113],"Here,":[114],"we":[115],"present":[116],"direct":[118],"comparison":[119],"composite":[124,227,250],"applications":[130],"aiming":[131],"at":[132,180,188],"construction":[134],"global":[136],"PES":[137,147,187],"molecules.":[140],"We":[141,169,191],"show":[142],"that":[143,171,194,243,255],"trained":[150,174,214],"much":[151],"more":[152,230],"efficiently":[153],"better":[156],"generalization":[157],"accuracy":[158],"without":[159],"relying":[160],"any":[162],"illustrate":[170,193],"distributions":[176],"points":[179],"low":[181],"energies":[182],"produce":[183],"accurate":[184,231],"predictions":[185],"high":[189],"energies.":[190],"also":[192],"extrapolate":[198],"input":[201],"space":[203],"building":[205],"free":[207],"surface":[209],"Heisenberg":[212],"paramagnetic":[217],"phase":[218],"validated":[220],"ferromagnetic":[223],"phase.":[224],"By":[225],"construction,":[226],"than":[233],"fixed":[237],"form.":[239],"Therefore,":[240],"illustrating":[242],"outperform":[246],"kernels,":[251],"our":[252],"work":[253],"suggests":[254],"should":[258],"preferred":[261],"PES.":[267]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1}],"updated_date":"2026-02-03T00:53:05.648605","created_date":"2025-10-10T00:00:00"}
