Issue 21, 2023

Screening of steam-reforming catalysts using unsupervised machine learning

Abstract

An efficient catalyst is crucial for the production of hydrogen through methanol reforming. However, the current study of catalysts for this reaction primarily relies on time-consuming and labor-intensive experimental methods. In this article, we propose a novel screening strategy called the Parameter-free Multi-view Clustering via Interactive Graph Mining (PMC–IGM) model to identify highly active methanol-reforming catalysts. Our approach involves the design of a new element representation method and clustering model, which effectively integrate and utilize information from different aspects of elements. The model achieved a predictive accuracy (ACC) of 61% and a Rand index (RI) of 97%. Notably, the model successfully identified two methanol-reforming catalysts with promising results, validating the efficiency and rationality of the algorithm. This research holds great significance in accelerating the discovery of novel methanol-reforming catalysts.

Graphical abstract: Screening of steam-reforming catalysts using unsupervised machine learning

Supplementary files

Article information

Article type
Paper
Submitted
31 Mae 2023
Accepted
23 Eost 2023
First published
31 Eost 2023

Catal. Sci. Technol., 2023,13, 6281-6290

Screening of steam-reforming catalysts using unsupervised machine learning

Y. Liu, Z. Liang, J. Huang, B. Zhong, X. Yang and T. Wang, Catal. Sci. Technol., 2023, 13, 6281 DOI: 10.1039/D3CY00754E

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