{"id":"https://openalex.org/W4388912652","doi":"https://doi.org/10.26434/chemrxiv-2023-k9rvm","title":"PyJCFit: A Non-Linear Regression Random Search Algorithm for Chemistry Data Fitting","display_name":"PyJCFit: A Non-Linear Regression Random Search Algorithm for Chemistry Data Fitting","publication_year":2023,"publication_date":"2023-11-22","ids":{"openalex":"https://openalex.org/W4388912652","doi":"https://doi.org/10.26434/chemrxiv-2023-k9rvm"},"language":"en","primary_location":{"id":"doi:10.26434/chemrxiv-2023-k9rvm","doi":"https://doi.org/10.26434/chemrxiv-2023-k9rvm","is_oa":true,"landing_page_url":"https://doi.org/10.26434/chemrxiv-2023-k9rvm","pdf_url":"https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/655d7cfedbd7c8b54bc25eb3/original/py-jc-fit-a-non-linear-regression-random-search-algorithm-for-chemistry-data-fitting.pdf","source":{"id":"https://openalex.org/S4393918830","display_name":"ChemRxiv","issn_l":"2573-2293","issn":["2573-2293"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"volume":null,"type":"preprint","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/655d7cfedbd7c8b54bc25eb3/original/py-jc-fit-a-non-linear-regression-random-search-algorithm-for-chemistry-data-fitting.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088937367","display_name":"Jixin Chen","orcid":"https://orcid.org/0000-0001-7381-0918","observed_orcids":["https://orcid.org/0000-0001-7381-0918","https://orcid.org/0000-0003-1763-8644"]},"institutions":[{"id":"https://openalex.org/I4210106879","display_name":"Ohio University","ror":"https://ror.org/01jr3y717","country_code":"US","type":"education","lineage":["https://openalex.org/I4210106879"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jixin Chen","raw_affiliation_strings":["Department of Chemistry and Biochemistry, Ohio University"],"raw_orcid":"https://orcid.org/0000-0001-7381-0918","affiliations":[{"raw_affiliation_string":"Department of Chemistry and Biochemistry, Ohio University","institution_ids":["https://openalex.org/I4210106879"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5088937367"],"corresponding_institution_ids":["https://openalex.org/I4210106879"],"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/T13650","display_name":"Computational Physics and Python Applications","score":0.5224999785423279,"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"}},"topics":[{"id":"https://openalex.org/T13650","display_name":"Computational Physics and Python Applications","score":0.5224999785423279,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.2224999964237213,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13487","display_name":"Statistical and numerical algorithms","score":0.15160000324249268,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/random-search","display_name":"random search","score":0.7879999876022339},{"id":"https://openalex.org/keywords/data-fitting","display_name":"data fitting","score":0.6000000238418579},{"id":"https://openalex.org/keywords/global-optimization","display_name":"global optimization","score":0.6850000023841858},{"id":"https://openalex.org/keywords/peak-detection","display_name":"peak detection","score":0.7720000147819519},{"id":"https://openalex.org/keywords/parameter-bounds","display_name":"parameter bounds","score":0.3959999978542328},{"id":"https://openalex.org/keywords/python","display_name":"Python","score":0.40799999237060547}],"concepts":[{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.7916249632835388},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5360351800918579},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5276702046394348},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.5153722167015076},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4774889647960663},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.47738975286483765},{"id":"https://openalex.org/C151376022","wikidata":"https://www.wikidata.org/wiki/Q168698","display_name":"Exponential function","level":2,"score":0.44648388028144836},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.41802626848220825},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.41631433367729187},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33451196551322937},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1489979326725006},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.13190793991088867},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.09959465265274048},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.26434/chemrxiv-2023-k9rvm","doi":"https://doi.org/10.26434/chemrxiv-2023-k9rvm","is_oa":true,"landing_page_url":"https://doi.org/10.26434/chemrxiv-2023-k9rvm","pdf_url":"https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/655d7cfedbd7c8b54bc25eb3/original/py-jc-fit-a-non-linear-regression-random-search-algorithm-for-chemistry-data-fitting.pdf","source":{"id":"https://openalex.org/S4393918830","display_name":"ChemRxiv","issn_l":"2573-2293","issn":["2573-2293"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.26434/chemrxiv-2023-k9rvm","doi":"https://doi.org/10.26434/chemrxiv-2023-k9rvm","is_oa":true,"landing_page_url":"https://doi.org/10.26434/chemrxiv-2023-k9rvm","pdf_url":"https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/655d7cfedbd7c8b54bc25eb3/original/py-jc-fit-a-non-linear-regression-random-search-algorithm-for-chemistry-data-fitting.pdf","source":{"id":"https://openalex.org/S4393918830","display_name":"ChemRxiv","issn_l":"2573-2293","issn":["2573-2293"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G3112304589","display_name":"Super-Resolution Optical Mapping for DNA Analysis Using Triplex-Forming Oligonucleotides as Stochastic Molecular Probes","funder_award_id":"R15HG009972","funder_id":"https://openalex.org/F4320337348","funder_display_name":"National Human Genome Research Institute"}],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337348","display_name":"National Human Genome Research Institute","ror":"https://ror.org/00baak391"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4388912652.pdf","grobid_xml":"https://content.openalex.org/works/W4388912652.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W632330365","https://openalex.org/W1490201909","https://openalex.org/W1974511160","https://openalex.org/W1975605689","https://openalex.org/W1982667094","https://openalex.org/W1987027200","https://openalex.org/W1996601324","https://openalex.org/W1996636277","https://openalex.org/W2011301426","https://openalex.org/W2035832119","https://openalex.org/W2051048551","https://openalex.org/W2054739227","https://openalex.org/W2062083462","https://openalex.org/W2085656746","https://openalex.org/W2087070363","https://openalex.org/W2097998348","https://openalex.org/W2101234009","https://openalex.org/W2128084896","https://openalex.org/W2130942532","https://openalex.org/W2256578114","https://openalex.org/W2335526184","https://openalex.org/W2460282195","https://openalex.org/W2772626348","https://openalex.org/W2922297107","https://openalex.org/W2942032697","https://openalex.org/W3099878876","https://openalex.org/W3103145119","https://openalex.org/W3207031770","https://openalex.org/W4200394859","https://openalex.org/W4205898344","https://openalex.org/W4252318916","https://openalex.org/W4292484204"],"related_works":["https://openalex.org/W2966151602","https://openalex.org/W258037142","https://openalex.org/W2033246008","https://openalex.org/W2045344972","https://openalex.org/W2896353007","https://openalex.org/W2082852755","https://openalex.org/W1497488672","https://openalex.org/W2501043830","https://openalex.org/W7135197767","https://openalex.org/W3091937660"],"abstract_inverted_index":{"Non-linear":[0],"least-square":[1],"regression":[2],"is":[3,18,57,83],"a":[4,35,53,114],"common":[5],"fitting":[6,44],"method":[7],"for":[8],"data":[9],"analysis":[10],"in":[11,26,63,89],"chemistry,":[12],"physics,":[13],"biology,":[14],"and":[15,96,118],"engineering.":[16],"It":[17],"sometimes":[19],"challenging":[20],"to":[21,50,58,74,111],"set":[22,51],"up":[23,52],"the":[24,75,79,105],"problem":[25],"existing":[27],"software":[28],"packages.":[29],"In":[30],"this":[31],"report,":[32],"I":[33],"introduce":[34],"na\u00efve":[36,106],"dynamic":[37],"grid":[38],"searching":[39],"algorithm":[40],"named":[41],"Python":[42],"jump-chain":[43],"(PyJCFit)":[45],"that":[46],"makes":[47],"it":[48],"easy":[49],"search.":[54],"The":[55],"idea":[56],"sequentially":[59],"search":[60],"all":[61],"parameters":[62],"trusted":[64],"bounds":[65],"with":[66,92],"an":[67],"exponential":[68],"distribution":[69],"of":[70,78,116],"space":[71],"paying":[72],"attention":[73],"neighbor":[76],"area":[77],"guessed":[80],"value.":[81],"PyJCFit":[82],"slow":[84],"but":[85],"carries":[86],"significant":[87],"advantages":[88],"compound":[90],"equations":[91],"breaks,":[93],"peak":[94],"searching,":[95],"global":[97],"fitting.":[98],"Various":[99],"scoring":[100],"functions":[101],"are":[102],"allowed":[103],"because":[104],"function":[107],"doesn\u2019t":[108],"care":[109],"what":[110],"optimize,":[112],"even":[113],"combination":[115],"well":[117],"poor-behaved":[119],"functions.":[120]},"counts_by_year":[],"updated_date":"2026-11-03T10:50:19.383937","created_date":"2023-11-23T00:00:00"}