{"title":"在机器学习的帮助下捕捉催化剂-支持相互作用的复杂性","authors":"Andrew S. Rosen","doi":"10.1002/anie.202521310","DOIUrl":null,"url":null,"abstract":"The structure of metal nanoparticles is central to their catalytic activity, but metal–support interactions are difficult to model via quantum‐mechanical calculations. Using a machine‐learned potential to model supported silver nanoparticles, it has been shown that the idealized nanoparticle shapes commonly invoked in the literature do not reflect experiments for diameters below 8 nm, as reported by Maxson and Szilvási.","PeriodicalId":125,"journal":{"name":"Angewandte Chemie International Edition","volume":"58 1","pages":""},"PeriodicalIF":16.9000,"publicationDate":"2025-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Capturing the Complexities of Catalyst–Support Interactions with the Help of Machine Learning\",\"authors\":\"Andrew S. Rosen\",\"doi\":\"10.1002/anie.202521310\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The structure of metal nanoparticles is central to their catalytic activity, but metal–support interactions are difficult to model via quantum‐mechanical calculations. Using a machine‐learned potential to model supported silver nanoparticles, it has been shown that the idealized nanoparticle shapes commonly invoked in the literature do not reflect experiments for diameters below 8 nm, as reported by Maxson and Szilvási.\",\"PeriodicalId\":125,\"journal\":{\"name\":\"Angewandte Chemie International Edition\",\"volume\":\"58 1\",\"pages\":\"\"},\"PeriodicalIF\":16.9000,\"publicationDate\":\"2025-10-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Angewandte Chemie International Edition\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://doi.org/10.1002/anie.202521310\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Angewandte Chemie International Edition","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1002/anie.202521310","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Capturing the Complexities of Catalyst–Support Interactions with the Help of Machine Learning
The structure of metal nanoparticles is central to their catalytic activity, but metal–support interactions are difficult to model via quantum‐mechanical calculations. Using a machine‐learned potential to model supported silver nanoparticles, it has been shown that the idealized nanoparticle shapes commonly invoked in the literature do not reflect experiments for diameters below 8 nm, as reported by Maxson and Szilvási.
期刊介绍:
Angewandte Chemie, a journal of the German Chemical Society (GDCh), maintains a leading position among scholarly journals in general chemistry with an impressive Impact Factor of 16.6 (2022 Journal Citation Reports, Clarivate, 2023). Published weekly in a reader-friendly format, it features new articles almost every day. Established in 1887, Angewandte Chemie is a prominent chemistry journal, offering a dynamic blend of Review-type articles, Highlights, Communications, and Research Articles on a weekly basis, making it unique in the field.