{"id":"https://openalex.org/W6966825936","doi":"https://doi.org/10.48550/arxiv.2402.19462","title":"Accelerating materials discovery for polymer solar cells: Data-driven insights enabled by natural language processing","display_name":"Accelerating materials discovery for polymer solar cells: Data-driven insights enabled by natural language processing","publication_year":2024,"publication_date":"2024-02-29","ids":{"openalex":"https://openalex.org/W6966825936","doi":"https://doi.org/10.48550/arxiv.2402.19462"},"language":"en","primary_location":{"id":"doi:10.48550/arxiv.2402.19462","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2402.19462","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":false,"raw_source_name":null,"raw_type":"article-journal"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2402.19462","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Shetty, Pranav","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Shetty, Pranav","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Adeboye, Aishat","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Adeboye, Aishat","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Gupta, Sonakshi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gupta, Sonakshi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Zhang, Chao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Chao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Ramprasad, Rampi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ramprasad, Rampi","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"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":true,"primary_topic":{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9948999881744385,"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":0.9948999881744385,"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/T11471","display_name":"Block Copolymer Self-Assembly","score":0.0008999999845400453,"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/T11272","display_name":"Nanowire Synthesis and Applications","score":0.0005000000237487257,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.7228999733924866},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.5453000068664551},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.39169999957084656},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.37860000133514404},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.3555000126361847},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.35100001096725464},{"id":"https://openalex.org/keywords/materials-informatics","display_name":"Materials informatics","score":0.34439998865127563},{"id":"https://openalex.org/keywords/material-properties","display_name":"Material properties","score":0.335999995470047}],"concepts":[{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.7228999733924866},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6290000081062317},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.5453000068664551},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41190001368522644},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.39169999957084656},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.37860000133514404},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3781999945640564},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.3555000126361847},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.35100001096725464},{"id":"https://openalex.org/C62085286","wikidata":"https://www.wikidata.org/wiki/Q6786605","display_name":"Materials informatics","level":5,"score":0.34439998865127563},{"id":"https://openalex.org/C31555180","wikidata":"https://www.wikidata.org/wiki/Q3523867","display_name":"Material properties","level":2,"score":0.335999995470047},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.3278999924659729},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.325300008058548},{"id":"https://openalex.org/C138827492","wikidata":"https://www.wikidata.org/wiki/Q6661985","display_name":"Data processing","level":2,"score":0.31540000438690186},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.3068000078201294},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.2985999882221222},{"id":"https://openalex.org/C2909316542","wikidata":"https://www.wikidata.org/wiki/Q422649","display_name":"Natural polymers","level":3,"score":0.29750001430511475},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.2971999943256378},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C2987263936","wikidata":"https://www.wikidata.org/wiki/Q10843872","display_name":"Materials processing","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.26260000467300415},{"id":"https://openalex.org/C2777618391","wikidata":"https://www.wikidata.org/wiki/Q1483757","display_name":"Solar power","level":3,"score":0.2567000091075897},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25589999556541443},{"id":"https://openalex.org/C183696295","wikidata":"https://www.wikidata.org/wiki/Q2487696","display_name":"Biochemical engineering","level":1,"score":0.2540999948978424},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2402.19462","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2402.19462","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":"article-journal"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2402.19462","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2402.19462","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":false,"raw_source_name":null,"raw_type":"article-journal"},"sustainable_development_goals":[{"score":0.6532312035560608,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0,113,139],"present":[1],"a":[2,28,66,78,142,193],"simulation":[3],"of":[4,12,178],"various":[5],"active":[6,166],"learning":[7,117,167],"strategies":[8,168],"for":[9,55,196],"the":[10,22,121,185],"discovery":[11,57,70],"polymer":[13],"solar":[14],"cell":[15],"donor/acceptor":[16],"pairs":[17],"using":[18],"data":[19,91,108,153],"extracted":[20,150],"from":[21,92,146],"literature":[23,148],"spanning":[24],"$\\sim$20":[25],"years":[26],"by":[27,72,111],"natural":[29],"language":[30],"processing":[31],"pipeline.":[32],"While":[33],"data-driven":[34,161,197],"methods":[35],"have":[36,51],"been":[37,53],"well":[38],"established":[39],"to":[40,77,89,119,129,149,159,173,184],"discover":[41],"novel":[42],"materials":[43,200],"faster":[44],"than":[45,94,106],"Edisonian":[46],"trial-and-error":[47],"approaches,":[48],"their":[49],"benefits":[50],"not":[52],"quantified":[54],"material":[56,83,151,179,187],"problems":[58],"that":[59,134,144,169],"can":[60,170],"take":[61],"decades.":[62],"Our":[63,85,163],"approach":[64],"demonstrates":[65],"potential":[67],"reduction":[68],"in":[69,82,155,199],"time":[71],"approximately":[73],"75":[74],"%,":[75],"equivalent":[76],"15":[79],"year":[80],"acceleration":[81],"innovation.":[84],"pipeline":[86,143],"enables":[87],"us":[88],"extract":[90],"greater":[93],"3300":[95],"papers":[96],"which":[97,154],"is":[98,157],"$\\sim$5":[99],"times":[100],"larger":[101],"and":[102,125],"therefore":[103],"more":[104],"diverse":[105],"similar":[107],"sets":[109],"reported":[110],"others.":[112],"also":[114],"trained":[115],"machine":[116],"models":[118,177],"predict":[120],"power":[122],"conversion":[123],"efficiency":[124],"used":[126,158,172],"our":[127],"model":[128],"identify":[130],"promising":[131],"donor-acceptor":[132],"combinations":[133],"are":[135],"as":[136],"yet":[137],"unreported.":[138],"thus":[140],"demonstrate":[141],"goes":[145],"published":[147],"property":[152],"turn":[156],"obtain":[160],"insights.":[162],"insights":[164],"include":[165],"be":[171,182],"train":[174],"strong":[175],"predictive":[176],"properties":[180],"or":[181],"robust":[183],"initial":[186],"system":[188],"used.":[189],"This":[190],"work":[191],"provides":[192],"valuable":[194],"framework":[195],"research":[198],"science.":[201]},"counts_by_year":[],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}
