{"title":"超越化学工程建模的第四范式","authors":"John R. Kitchin, Victor Alves, Carl D. Laird","doi":"10.1038/s44286-024-00170-x","DOIUrl":null,"url":null,"abstract":"Differentiable programming underpins the foundations of machine learning, and enables new approaches to solving chemical engineering problems. This Comment discusses the opportunities and challenges in education and preparing the workforce to leverage these tools. Integration of these skills with domain knowledge can have a substantial impact on the future of chemical engineering.","PeriodicalId":501699,"journal":{"name":"Nature Chemical Engineering","volume":"2 1","pages":"11-13"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44286-024-00170-x.pdf","citationCount":"0","resultStr":"{\"title\":\"Beyond the fourth paradigm of modeling in chemical engineering\",\"authors\":\"John R. Kitchin, Victor Alves, Carl D. Laird\",\"doi\":\"10.1038/s44286-024-00170-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Differentiable programming underpins the foundations of machine learning, and enables new approaches to solving chemical engineering problems. This Comment discusses the opportunities and challenges in education and preparing the workforce to leverage these tools. Integration of these skills with domain knowledge can have a substantial impact on the future of chemical engineering.\",\"PeriodicalId\":501699,\"journal\":{\"name\":\"Nature Chemical Engineering\",\"volume\":\"2 1\",\"pages\":\"11-13\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-01-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.nature.com/articles/s44286-024-00170-x.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Chemical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.nature.com/articles/s44286-024-00170-x\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Chemical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://www.nature.com/articles/s44286-024-00170-x","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Beyond the fourth paradigm of modeling in chemical engineering
Differentiable programming underpins the foundations of machine learning, and enables new approaches to solving chemical engineering problems. This Comment discusses the opportunities and challenges in education and preparing the workforce to leverage these tools. Integration of these skills with domain knowledge can have a substantial impact on the future of chemical engineering.
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