Capturing the Complexities of Catalyst–Support Interactions with the Help of Machine Learning

Andrew S. Rosen

doi:10.1002/anie.202521310

Capturing the Complexities of Catalyst–Support Interactions with the Help of Machine Learning

Andrew S. Rosen

Research output: Contribution to journal › Comment/debate › peer-review

1 Scopus citations

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.

Original language	English (US)
Article number	e202521310
Journal	Angewandte Chemie - International Edition
Volume	64
Issue number	49
DOIs	https://doi.org/10.1002/anie.202521310
State	Published - Dec 1 2025
Externally published	Yes

All Science Journal Classification (ASJC) codes

Catalysis
General Chemistry

Keywords

Catalysis
Density functional theory
Interatomic potential
Machine learning
Nanoparticle

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10.1002/anie.202521310

Cite this

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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 Szilv{\'a}si.",

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AB - 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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KW - Density functional theory

KW - Interatomic potential

KW - Machine learning

KW - Nanoparticle

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Capturing the Complexities of Catalyst–Support Interactions with the Help of Machine Learning

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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