Issue 22, 2024

Unsupervised pattern recognition on the surface of simulated metal nanoparticles for catalytic applications

Abstract

The application of supervised machine learning to the study of catalytic metal nanoparticles has been shown to deliver excellent performance for a range of predictive tasks. However, this success assumes that the particles have been thoroughly characterised and that the property labels are known. Even in exclusively computational studies, the labelling of metal nanoparticles remains the bottleneck for most machine learning studies due to either high computational costs or low relevance to the experimental properties of interest. To facilitate more widespread use of machine learning in catalysis, a computationally affordable strategy to describe metal nanoparticles by a label that is relevant to their catalytic activities is needed. In this study we propose an entirely data-driven approach that can be automated to characterise the patterns and catalytic activities of the surface atoms of simulated metal nanoparticles, and evaluate its utility for catalytic applications.

Graphical abstract: Unsupervised pattern recognition on the surface of simulated metal nanoparticles for catalytic applications

Supplementary files

Article information

Article type
Paper
Submitted
15 Aug 2024
Accepted
21 Sep 2024
First published
01 Oct 2024

Catal. Sci. Technol., 2024,14, 6651-6661

Unsupervised pattern recognition on the surface of simulated metal nanoparticles for catalytic applications

J. Y. C. Ting, G. Opletal and A. S. Barnard, Catal. Sci. Technol., 2024, 14, 6651 DOI: 10.1039/D4CY01000K

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements