{"title":"加速电子传输和超导预测。","authors":"Ting Cao","doi":"10.1038/s43588-024-00678-5","DOIUrl":null,"url":null,"abstract":"By developing a machine learning framework, a recent study substantially accelerates the calculation of electron–phonon coupling, making it computationally feasible to predict and understand a range of important physical phenomena, including electronic transport, hot-carrier relaxation, and superconductivity in complex materials.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":null,"pages":null},"PeriodicalIF":12.0000,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Accelerating predictions of electronic transport and superconductivity\",\"authors\":\"Ting Cao\",\"doi\":\"10.1038/s43588-024-00678-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"By developing a machine learning framework, a recent study substantially accelerates the calculation of electron–phonon coupling, making it computationally feasible to predict and understand a range of important physical phenomena, including electronic transport, hot-carrier relaxation, and superconductivity in complex materials.\",\"PeriodicalId\":74246,\"journal\":{\"name\":\"Nature computational science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":12.0000,\"publicationDate\":\"2024-08-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature computational science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.nature.com/articles/s43588-024-00678-5\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature computational science","FirstCategoryId":"1085","ListUrlMain":"https://www.nature.com/articles/s43588-024-00678-5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Accelerating predictions of electronic transport and superconductivity
By developing a machine learning framework, a recent study substantially accelerates the calculation of electron–phonon coupling, making it computationally feasible to predict and understand a range of important physical phenomena, including electronic transport, hot-carrier relaxation, and superconductivity in complex materials.
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