Issue 47, 2022

Rational ensemble design of alloy catalysts for selective ammonia oxidation based on machine learning

Abstract

High-throughput computation and machine learning studies are conducted for the rational design of ensembles of alloy catalysts for selective catalytic oxidation of NH3 (NH3-SCO). High-throughput calculation is first performed for the binding energy of intermediates on all possible sites of low-index facets. The general features from intrinsic information of the surface, adsorbate, and binding site are proposed for binding energy prediction on alloy catalysts, and highly accurate tree-based ensemble machine learning models with low MAE of <0.15 eV and high R2 of ≥0.97 are obtained for binding energy prediction of key intermediates involved in NH3-SCO. Furthermore, binding energies of stable configurations of pivotal intermediates in NH3-SCO are extracted to construct the new data set without known labels about catalytic performance, and unsupervised clustering analyses are conducted twice to quickly categorize ensemble systems into different sets for searching promising candidate catalysts. The different clusters are identified, and primary elementary processes of NH3-SCO on representative ensemble systems of the clusters are studied to further reveal the properties of clusters and the clusters with promising catalysts are found ultimately. Our theoretical study could provide a novel strategy for the rational design of catalysts and accelerate the discovery of new alloy catalysts for NH3-SCO.

Graphical abstract: Rational ensemble design of alloy catalysts for selective ammonia oxidation based on machine learning

Supplementary files

Article information

Article type
Paper
Submitted
14 Sep 2022
Accepted
06 Nov 2022
First published
07 Nov 2022

J. Mater. Chem. A, 2022,10, 25238-25248

Rational ensemble design of alloy catalysts for selective ammonia oxidation based on machine learning

J. Yang, Z. Wang, Z. Liu, Q. Wang, Y. Wen, A. Zhang, R. Chen and B. Shan, J. Mater. Chem. A, 2022, 10, 25238 DOI: 10.1039/D2TA07216E

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