iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://api.openalex.org/works/doi:10.48550/ARXIV.2302.02595
{"id":"https://openalex.org/W4319453296","doi":"https://doi.org/10.48550/arxiv.2302.02595","title":"Clarifying Trust of Materials Property Predictions using Neural Networks with Distribution-Specific Uncertainty Quantification","display_name":"Clarifying Trust of Materials Property Predictions using Neural Networks with Distribution-Specific Uncertainty Quantification","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4319453296","doi":"https://doi.org/10.48550/arxiv.2302.02595"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2302.02595","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":["Cornell University"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/2302.02595","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011908450","display_name":"Cameron Gruich","orcid":"https://orcid.org/0000-0002-3801-1296"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gruich, Cameron","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090564214","display_name":"Varun Madhavan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Madhavan, Varun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100328536","display_name":"Yixin Wang","orcid":"https://orcid.org/0000-0002-2789-7909"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yixin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5026770434","display_name":"Bryan R. Goldsmith","orcid":"https://orcid.org/0000-0003-1264-8018"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Goldsmith, Bryan","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"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":1,"citation_normalized_percentile":{"value":0.824426,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":68,"max":79},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9997,"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":0.9997,"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/T10192","display_name":"Catalytic Processes in Materials Science","score":0.9269,"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/T12039","display_name":"Electron and X-Ray Spectroscopy Techniques","score":0.9163,"subfield":{"id":"https://openalex.org/subfields/2508","display_name":"Surfaces, Coatings and Films"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/trustworthiness","display_name":"Trustworthiness","score":0.61030084},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty Quantification","score":0.59798944}],"concepts":[{"id":"https://openalex.org/C153701036","wikidata":"https://www.wikidata.org/wiki/Q659974","display_name":"Trustworthiness","level":2,"score":0.61030084},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.59798944},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.5756323},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5675198},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.5499777},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.50592655},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.48014447},{"id":"https://openalex.org/C175583648","wikidata":"https://www.wikidata.org/wiki/Q903156","display_name":"Heterogeneous catalysis","level":3,"score":0.42835462},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36743963},{"id":"https://openalex.org/C161790260","wikidata":"https://www.wikidata.org/wiki/Q82264","display_name":"Catalysis","level":2,"score":0.3570984},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31194246},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.29097396},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1431795},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","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},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2302.02595","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":["Cornell University"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2302.02595","pdf_url":"http://arxiv.org/pdf/2302.02595","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":false,"landing_page_url":"https://api.datacite.org/dois/10.48550/arxiv.2302.02595","pdf_url":null,"source":{"id":"https://openalex.org/S4393179698","display_name":"DataCite API","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210145204","host_organization_name":"DataCite","host_organization_lineage":["https://openalex.org/I4210145204"],"host_organization_lineage_names":["DataCite"],"type":"metadata"},"license":null,"license_id":null,"version":null}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2302.02595","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":["Cornell University"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.66,"display_name":"Affordable and clean energy"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W90316445","https://openalex.org/W4327743613","https://openalex.org/W4293226380","https://openalex.org/W3199750033","https://openalex.org/W3163373470","https://openalex.org/W2965447900","https://openalex.org/W2374509987","https://openalex.org/W2078622645","https://openalex.org/W2076536433","https://openalex.org/W1991093342"],"abstract_inverted_index":{"It":[0],"is":[1,104,116,143],"critical":[2],"that":[3],"machine":[4],"learning":[5],"(ML)":[6],"model":[7,141],"predictions":[8],"be":[9,119,146],"trustworthy":[10,128],"for":[11,123,131],"high-throughput":[12],"catalyst":[13,77],"discovery":[14],"approaches.":[15],"Uncertainty":[16],"quantification":[17],"(UQ)":[18],"methods":[19,31,48,83],"allow":[20],"estimation":[21],"of":[22,25,40,62,100,140,152],"the":[23,38,67,73,85],"trustworthiness":[24],"an":[26],"ML":[27],"model,":[28],"but":[29],"these":[30],"have":[32],"not":[33],"been":[34],"well":[35],"explored":[36],"in":[37,148],"field":[39],"heterogeneous":[41,76,132],"catalysis.":[42],"Herein,":[43],"we":[44],"investigate":[45],"different":[46],"UQ":[47,82,102,129],"applied":[49],"to":[50,58,84,118,145],"a":[51,120],"crystal":[52],"graph":[53],"convolutional":[54],"neural":[55,137],"network":[56],"(CGCNN)":[57],"predict":[59],"adsorption":[60,86],"energies":[61],"molecules":[63],"on":[64,107],"alloys":[65],"from":[66],"Open":[68],"Catalyst":[69],"2020":[70],"(OC20)":[71],"dataset,":[72],"largest":[74],"existing":[75],"dataset.":[78],"We":[79],"apply":[80],"three":[81],"energy":[87],"predictions,":[88],"namely":[89],"k-fold":[90],"ensembling,":[91],"Monte":[92],"Carlo":[93],"dropout,":[94],"and":[95,112],"evidential":[96],"regression.":[97],"The":[98],"effectiveness":[99],"each":[101],"method":[103],"assessed":[105],"based":[106],"accuracy,":[108],"sharpness,":[109],"dispersion,":[110],"calibration,":[111],"tightness.":[113],"Evidential":[114],"regression":[115],"demonstrated":[117],"powerful":[121],"approach":[122],"rapidly":[124],"obtaining":[125],"tunable,":[126],"competitively":[127],"estimates":[130],"catalysis":[133],"applications":[134,151],"when":[135],"using":[136,154],"networks.":[138],"Recalibration":[139],"uncertainties":[142],"shown":[144],"essential":[147],"practical":[149],"screening":[150],"catalysts":[153],"uncertainties.":[155]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4319453296","counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2024-12-07T23:30:25.590432","created_date":"2023-02-09"}