{"title":"用重新配方再现颜色","authors":"Jinming Fan , Chao Qian , Shaodong Zhou","doi":"10.1016/j.aichem.2023.100003","DOIUrl":null,"url":null,"abstract":"<div><p>A reverse molecule contribution (reMC) - molecule contribution (MC) – Machine learning (ML) protocol for disassemble and reproduce the spectrum is presented. By splitting the mixture spectrum with monochromophoric spectra in the database in a “Peeling-Onion” manner, a new recipe can be obtained. Upon comparison of the reproduced spectrum (with the forward molecular contribution - machine learning method) with the original one, the reliability of the proposed method is justified.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Reproducing the color with reformulated recipe\",\"authors\":\"Jinming Fan , Chao Qian , Shaodong Zhou\",\"doi\":\"10.1016/j.aichem.2023.100003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>A reverse molecule contribution (reMC) - molecule contribution (MC) – Machine learning (ML) protocol for disassemble and reproduce the spectrum is presented. By splitting the mixture spectrum with monochromophoric spectra in the database in a “Peeling-Onion” manner, a new recipe can be obtained. Upon comparison of the reproduced spectrum (with the forward molecular contribution - machine learning method) with the original one, the reliability of the proposed method is justified.</p></div>\",\"PeriodicalId\":72302,\"journal\":{\"name\":\"Artificial intelligence chemistry\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Artificial intelligence chemistry\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2949747723000039\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial intelligence chemistry","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949747723000039","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A reverse molecule contribution (reMC) - molecule contribution (MC) – Machine learning (ML) protocol for disassemble and reproduce the spectrum is presented. By splitting the mixture spectrum with monochromophoric spectra in the database in a “Peeling-Onion” manner, a new recipe can be obtained. Upon comparison of the reproduced spectrum (with the forward molecular contribution - machine learning method) with the original one, the reliability of the proposed method is justified.
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