{"id":"https://openalex.org/W4311564419","doi":"https://doi.org/10.1088/2632-2153/acac01","title":"Building robust machine learning models for small chemical science data: the case of shear viscosity of fluids","display_name":"Building robust machine learning models for small chemical science data: the case of shear viscosity of fluids","publication_year":2022,"publication_date":"2022-12-01","ids":{"openalex":"https://openalex.org/W4311564419","doi":"https://doi.org/10.1088/2632-2153/acac01"},"language":"en","primary_location":{"id":"doi:10.1088/2632-2153/acac01","is_oa":true,"landing_page_url":"https://doi.org/10.1088/2632-2153/acac01","pdf_url":"https://iopscience.iop.org/article/10.1088/2632-2153/acac01/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/acac01/pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5022004866","display_name":"Nikhil V. S. Avula","orcid":"https://orcid.org/0000-0001-6467-6040"},"institutions":[{"id":"https://openalex.org/I147481903","display_name":"Jawaharlal Nehru Centre for Advanced Scientific Research","ror":"https://ror.org/0538gdx71","country_code":"IN","type":"facility","lineage":["https://openalex.org/I147481903","https://openalex.org/I2799351866","https://openalex.org/I4210098748","https://openalex.org/I4210134808"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Nikhil V S Avula","raw_affiliation_strings":["Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India"],"raw_orcid":"https://orcid.org/0000-0001-6467-6040","affiliations":[{"raw_affiliation_string":"Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India","institution_ids":["https://openalex.org/I147481903"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078137707","display_name":"Shivanand Kumar Veesam","orcid":"https://orcid.org/0000-0003-4671-9646"},"institutions":[{"id":"https://openalex.org/I147481903","display_name":"Jawaharlal Nehru Centre for Advanced Scientific Research","ror":"https://ror.org/0538gdx71","country_code":"IN","type":"facility","lineage":["https://openalex.org/I147481903","https://openalex.org/I2799351866","https://openalex.org/I4210098748","https://openalex.org/I4210134808"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Shivanand Kumar Veesam","raw_affiliation_strings":["Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India"],"raw_orcid":"https://orcid.org/0000-0003-4671-9646","affiliations":[{"raw_affiliation_string":"Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India","institution_ids":["https://openalex.org/I147481903"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027271884","display_name":"Sudarshan Behera","orcid":"https://orcid.org/0000-0003-1025-0639"},"institutions":[{"id":"https://openalex.org/I147481903","display_name":"Jawaharlal Nehru Centre for Advanced Scientific Research","ror":"https://ror.org/0538gdx71","country_code":"IN","type":"facility","lineage":["https://openalex.org/I147481903","https://openalex.org/I2799351866","https://openalex.org/I4210098748","https://openalex.org/I4210134808"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sudarshan Behera","raw_affiliation_strings":["Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India"],"raw_orcid":"https://orcid.org/0000-0003-1025-0639","affiliations":[{"raw_affiliation_string":"Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India","institution_ids":["https://openalex.org/I147481903"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013255727","display_name":"Sundaram Balasubramanian","orcid":"https://orcid.org/0000-0002-3355-6764"},"institutions":[{"id":"https://openalex.org/I147481903","display_name":"Jawaharlal Nehru Centre for Advanced Scientific Research","ror":"https://ror.org/0538gdx71","country_code":"IN","type":"facility","lineage":["https://openalex.org/I147481903","https://openalex.org/I2799351866","https://openalex.org/I4210098748","https://openalex.org/I4210134808"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sundaram Balasubramanian","raw_affiliation_strings":["Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India"],"raw_orcid":"https://orcid.org/0000-0002-3355-6764","affiliations":[{"raw_affiliation_string":"Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India","institution_ids":["https://openalex.org/I147481903"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5022004866"],"corresponding_institution_ids":["https://openalex.org/I147481903"],"apc_list":{"value":1600,"currency":"GBP","value_usd":1962},"apc_paid":{"value":1600,"currency":"GBP","value_usd":1962},"fwci":0.5275,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.58213045,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"3","issue":"4","first_page":"045032","last_page":"045032"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.991100013256073,"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":0.991100013256073,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9814000129699707,"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"}},{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9758999943733215,"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/overfitting","display_name":"Overfitting","score":0.8040143251419067},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.6330970525741577},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5960003733634949},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5344418883323669},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4683893024921417},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.46322014927864075},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.45903587341308594},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4517185688018799},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.41872453689575195},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.41622719168663025},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3804377019405365},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.31305912137031555},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.18340399861335754},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.09507095813751221}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8040143251419067},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.6330970525741577},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5960003733634949},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5344418883323669},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4683893024921417},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.46322014927864075},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.45903587341308594},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4517185688018799},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.41872453689575195},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.41622719168663025},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3804377019405365},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31305912137031555},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.18340399861335754},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.09507095813751221},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1088/2632-2153/acac01","is_oa":true,"landing_page_url":"https://doi.org/10.1088/2632-2153/acac01","pdf_url":"https://iopscience.iop.org/article/10.1088/2632-2153/acac01/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"}],"best_oa_location":{"id":"doi:10.1088/2632-2153/acac01","is_oa":true,"landing_page_url":"https://doi.org/10.1088/2632-2153/acac01","pdf_url":"https://iopscience.iop.org/article/10.1088/2632-2153/acac01/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":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4311564419.pdf"},"referenced_works_count":136,"referenced_works":["https://openalex.org/W1497792867","https://openalex.org/W1614738390","https://openalex.org/W1964131172","https://openalex.org/W1975352716","https://openalex.org/W1981552604","https://openalex.org/W1987410264","https://openalex.org/W1989202633","https://openalex.org/W1990835574","https://openalex.org/W1998260904","https://openalex.org/W2002207707","https://openalex.org/W2006061247","https://openalex.org/W2007168516","https://openalex.org/W2017019577","https://openalex.org/W2026824277","https://openalex.org/W2034615695","https://openalex.org/W2039516744","https://openalex.org/W2041175065","https://openalex.org/W2041902442","https://openalex.org/W2049579077","https://openalex.org/W2055250702","https://openalex.org/W2057089150","https://openalex.org/W2063603310","https://openalex.org/W2075965721","https://openalex.org/W2081585228","https://openalex.org/W2083343428","https://openalex.org/W2084652263","https://openalex.org/W2085890279","https://openalex.org/W2091433464","https://openalex.org/W2101234009","https://openalex.org/W2108995755","https://openalex.org/W2110118110","https://openalex.org/W2114704115","https://openalex.org/W2116176617","https://openalex.org/W2118796860","https://openalex.org/W2118865162","https://openalex.org/W2138095345","https://openalex.org/W2141007997","https://openalex.org/W2151554678","https://openalex.org/W2154290668","https://openalex.org/W2154776925","https://openalex.org/W2157506489","https://openalex.org/W2176009755","https://openalex.org/W2335833753","https://openalex.org/W2412615290","https://openalex.org/W2505756961","https://openalex.org/W2558701269","https://openalex.org/W2589164756","https://openalex.org/W2605299208","https://openalex.org/W2605925159","https://openalex.org/W2613225389","https://openalex.org/W2782958502","https://openalex.org/W2783051902","https://openalex.org/W2788124719","https://openalex.org/W2791414526","https://openalex.org/W2801572038","https://openalex.org/W2804977958","https://openalex.org/W2809515679","https://openalex.org/W2887773743","https://openalex.org/W2891548662","https://openalex.org/W2896761873","https://openalex.org/W2898280479","https://openalex.org/W2899013536","https://openalex.org/W2906626571","https://openalex.org/W2940328394","https://openalex.org/W2944469030","https://openalex.org/W2947427925","https://openalex.org/W2954726078","https://openalex.org/W2959101608","https://openalex.org/W2963389298","https://openalex.org/W2963507686","https://openalex.org/W2973126098","https://openalex.org/W2981679558","https://openalex.org/W2988055229","https://openalex.org/W2989856808","https://openalex.org/W3000999892","https://openalex.org/W3003257820","https://openalex.org/W3004830748","https://openalex.org/W3012035124","https://openalex.org/W3012230641","https://openalex.org/W3014339631","https://openalex.org/W3026102978","https://openalex.org/W3035965352","https://openalex.org/W3043792974","https://openalex.org/W3044724994","https://openalex.org/W3046470356","https://openalex.org/W3081307347","https://openalex.org/W3090572406","https://openalex.org/W3099878876","https://openalex.org/W3104716259","https://openalex.org/W3104887532","https://openalex.org/W3108575536","https://openalex.org/W3109320111","https://openalex.org/W3113616921","https://openalex.org/W3119291344","https://openalex.org/W3122598275","https://openalex.org/W3122731408","https://openalex.org/W3124779408","https://openalex.org/W3129164071","https://openalex.org/W3129238713","https://openalex.org/W3130255721","https://openalex.org/W3139241485","https://openalex.org/W3142599470","https://openalex.org/W3159181932","https://openalex.org/W3163993681","https://openalex.org/W3167401768","https://openalex.org/W3170955283","https://openalex.org/W3178031510","https://openalex.org/W3187024189","https://openalex.org/W3197433610","https://openalex.org/W3200588771","https://openalex.org/W3209575515","https://openalex.org/W3209850868","https://openalex.org/W3210166225","https://openalex.org/W3212242419","https://openalex.org/W3215883361","https://openalex.org/W3216711780","https://openalex.org/W4200022889","https://openalex.org/W4220910131","https://openalex.org/W4220935484","https://openalex.org/W4220969947","https://openalex.org/W4238793347","https://openalex.org/W4242256426","https://openalex.org/W4280551776","https://openalex.org/W4280616326","https://openalex.org/W4283765318","https://openalex.org/W4285593189","https://openalex.org/W4285596585","https://openalex.org/W4290725232","https://openalex.org/W4290802350","https://openalex.org/W4323319736","https://openalex.org/W6671879489","https://openalex.org/W6674330103","https://openalex.org/W6675354045","https://openalex.org/W6681079989","https://openalex.org/W6840157977","https://openalex.org/W6845102520"],"related_works":["https://openalex.org/W1574414179","https://openalex.org/W4362597605","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4297676672","https://openalex.org/W4298369531","https://openalex.org/W3155135229","https://openalex.org/W2003394143","https://openalex.org/W4225271882","https://openalex.org/W2771206194"],"abstract_inverted_index":{"Abstract":[0],"Shear":[1],"viscosity,":[2],"though":[3],"being":[4],"a":[5,44,85,96,126,132,195,227],"fundamental":[6],"property":[7],"of":[8,59,84,124,147,198,210,218],"all":[9],"fluids,":[10],"is":[11,63,66],"computationally":[12],"expensive":[13],"to":[14,29,39,79,104,152,161,179,207,229,251,266,300],"calculate":[15],"from":[16,95,261],"equilibrium":[17],"molecular":[18,31],"dynamics":[19],"simulations.":[20],"Recently,":[21],"machine":[22],"learning":[23],"(ML)":[24],"methods":[25,50,240],"have":[26],"been":[27],"used":[28,120,250,265],"augment":[30],"simulations":[32],"in":[33,43,221,288,295],"many":[34],"contexts,":[35],"thus":[36],"showing":[37],"promise":[38],"estimate":[40,162,252],"viscosity":[41,83,284],"too":[42],"relatively":[45],"inexpensive":[46],"manner.":[47],"However,":[48],"ML":[49,77,212,232,274,302],"face":[51],"significant":[52],"challenges\u2014such":[53],"as":[54,65,168],"overfitting,":[55],"when":[56],"the":[57,60,67,81,101,118,136,144,154,163,216,231,253,273,292],"size":[58],"data":[61,98,129,139,285,306],"set":[62,130,286],"small,":[64],"case":[68],"with":[69,88],"viscosity.":[70],"In":[71,141],"this":[72,142,289,296],"work,":[73,297],"we":[74,115],"train":[75],"seven":[76],"models":[78,233,275,303],"predict":[80],"shear":[82],"Lennard\u2013Jones":[86],"fluid,":[87],"particular":[89],"emphasis":[90],"on":[91,235,255,281],"addressing":[92],"issues":[93,102],"arising":[94],"small":[97,305],"set.":[99],"Specifically,":[100],"related":[103],"model":[105,182],"selection,":[106],"performance":[107,121,185,219],"estimation":[108,122],"and":[109,184,187,223,225,247],"uncertainty":[110,242,254,259],"quantification":[111],"were":[112,263],"investigated.":[113],"First,":[114],"show":[116],"that":[117,189],"widely":[119],"procedure":[123,193],"using":[125,148,271],"single":[127],"unseen":[128],"shows":[131,194],"wide":[133],"variability\u2014in":[134],"estimating":[135],"errors":[137],"on\u2014small":[138],"sets.":[140,307],"context,":[143],"common":[145],"practice":[146],"cross":[149],"validation":[150],"(CV)":[151],"select":[153],"hyperparameters":[155],"(model":[156],"selection)":[157],"can":[158],"be":[159],"adapted":[160],"generalization":[164],"error":[165,199],"(performance":[166],"estimation)":[167],"well.":[169],"We":[170,214],"compare":[171],"two":[172,239],"simple":[173],"CV":[174,191,203],"procedures":[175,204,293],"for":[176,241,304],"their":[177],"ability":[178],"do":[180],"both":[181],"selection":[183],"estimation,":[186],"find":[188],"k-fold":[190],"based":[192,234],"lower":[196],"variance":[197],"estimates.":[200],"Also,":[201],"these":[202],"naturally":[205],"lead":[206,299],"an":[208,268,282],"ensemble":[209,248],"trained":[211],"models.":[213],"discuss":[215],"role":[217],"metrics":[220],"training":[222],"evaluation":[224],"propose":[226],"method":[228],"rank":[230],"multiple":[236],"metrics.":[237],"Finally,":[238],"quantification\u2014Gaussian":[243],"process":[244],"regression":[245],"(GPR)":[246],"method\u2014were":[249],"individual":[256],"predictions.":[257],"The":[258],"estimates":[260],"GPR":[262],"also":[264],"construct":[267],"applicability":[269],"domain":[270],"which":[272],"provided":[276],"even":[277],"more":[278],"reliable":[279],"predictions":[280],"independent":[283],"generated":[287],"work.":[290],"Overall,":[291],"prescribed":[294],"together,":[298],"robust":[301]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4}],"updated_date":"2026-05-22T06:13:13.366637","created_date":"2022-12-27T00:00:00"}