{"title":"通过机器学习增强综合预测。","authors":"J. C. Schön","doi":"10.1038/s43588-025-00771-3","DOIUrl":null,"url":null,"abstract":"Identifying promising synthesis targets and designing routes to their synthesis is a grand challenge in chemistry and materials science. Recent work employing machine learning in combination with traditional approaches is opening new ways to address this truly Herculean task.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 2","pages":"95-96"},"PeriodicalIF":12.0000,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancing synthesis prediction via machine learning\",\"authors\":\"J. C. Schön\",\"doi\":\"10.1038/s43588-025-00771-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Identifying promising synthesis targets and designing routes to their synthesis is a grand challenge in chemistry and materials science. Recent work employing machine learning in combination with traditional approaches is opening new ways to address this truly Herculean task.\",\"PeriodicalId\":74246,\"journal\":{\"name\":\"Nature computational science\",\"volume\":\"5 2\",\"pages\":\"95-96\"},\"PeriodicalIF\":12.0000,\"publicationDate\":\"2025-02-11\",\"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/s43588-025-00771-3\",\"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/s43588-025-00771-3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Enhancing synthesis prediction via machine learning
Identifying promising synthesis targets and designing routes to their synthesis is a grand challenge in chemistry and materials science. Recent work employing machine learning in combination with traditional approaches is opening new ways to address this truly Herculean task.