{"title":"预测可充电电池电解质分子特性的知识-数据双驱动框架","authors":"Yu-Chen Gao, Yu-Hang Yuan, Suozhi Huang, Nan Yao, Legeng Yu, Yao-Peng Chen, Qiang Zhang, Xiang Chen","doi":"10.1002/ange.202580461","DOIUrl":null,"url":null,"abstract":"<p>In their Research Article (e202416506), Xiang Chen and co-authors developed a knowledge–data dual-driven framework that incorporates domain expertise into artificial intelligence models, achieving notable accuracy in predicting properties such as melting, boiling, and flash points of battery electrolytes.\n <figure>\n <div><picture>\n <source></source></picture><p></p>\n </div>\n </figure>\n </p>","PeriodicalId":7803,"journal":{"name":"Angewandte Chemie","volume":"137 4","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ange.202580461","citationCount":"0","resultStr":"{\"title\":\"Frontispiz: A Knowledge–Data Dual-Driven Framework for Predicting the Molecular Properties of Rechargeable Battery Electrolytes\",\"authors\":\"Yu-Chen Gao, Yu-Hang Yuan, Suozhi Huang, Nan Yao, Legeng Yu, Yao-Peng Chen, Qiang Zhang, Xiang Chen\",\"doi\":\"10.1002/ange.202580461\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>In their Research Article (e202416506), Xiang Chen and co-authors developed a knowledge–data dual-driven framework that incorporates domain expertise into artificial intelligence models, achieving notable accuracy in predicting properties such as melting, boiling, and flash points of battery electrolytes.\\n <figure>\\n <div><picture>\\n <source></source></picture><p></p>\\n </div>\\n </figure>\\n </p>\",\"PeriodicalId\":7803,\"journal\":{\"name\":\"Angewandte Chemie\",\"volume\":\"137 4\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-01-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ange.202580461\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Angewandte Chemie\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/ange.202580461\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Angewandte Chemie","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/ange.202580461","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Frontispiz: A Knowledge–Data Dual-Driven Framework for Predicting the Molecular Properties of Rechargeable Battery Electrolytes
In their Research Article (e202416506), Xiang Chen and co-authors developed a knowledge–data dual-driven framework that incorporates domain expertise into artificial intelligence models, achieving notable accuracy in predicting properties such as melting, boiling, and flash points of battery electrolytes.
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