在机器学习的帮助下捕捉催化剂-支持相互作用的复杂性

IF 16.9 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Andrew S. Rosen
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引用次数: 0

摘要

金属纳米颗粒的结构是其催化活性的核心,但金属-载体相互作用很难通过量子力学计算来模拟。根据Maxson和Szilvási的报告,使用机器学习势来模拟支持的纳米银粒子,已经表明,在文献中通常调用的理想纳米粒子形状不能反映直径低于8纳米的实验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
CiteScore
26.60
自引率
6.60%
发文量
3549
审稿时长
1.5 months
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.
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