Efficient Machine-Learning-Aided Screening of Hydrogen Adsorption on Bimetallic Nanoclusters
Loading...
Access rights
openAccess
publishedVersion
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Date
2020-12-14
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
14
Series
ACS Combinatorial Science, Volume 22, issue 12, pp. 768-781
Abstract
Nanoclusters add an additional dimension in which to look for promising catalyst candidates, since catalytic activity of materials often changes at the nanoscale. However, the large search space of relevant atomic sites exacerbates the challenge for computational screening methods and requires the development of new techniques for efficient exploration. We present an automated workflow that systematically manages simulations from the generation of nanoclusters through the submission of production jobs, to the prediction of adsorption energies. The presented workflow was designed to screen nanoclusters of arbitrary shapes and size, but in this work the search was restricted to bimetallic icosahedral clusters and the adsorption was exemplified on the hydrogen evolution reaction. We demonstrate the efficient exploration of nanocluster configurations and screening of adsorption energies with the aid of machine learning. The results show that the maximum of the d-band Hilbert-transform ϵu is correlated strongly with adsorption energies and could be a useful screening property accessible at the nanocluster level.Description
Keywords
adsorption, catalysis, computational screening, hydrogen evolution reaction, machine learning, nanoclusters, workflow automation
Other note
Citation
Jager, M O J, Ranawat, Y S, Canova, F F, Morooka, E V & Foster, A S 2020, ' Efficient Machine-Learning-Aided Screening of Hydrogen Adsorption on Bimetallic Nanoclusters ', ACS Combinatorial Science, vol. 22, no. 12, pp. 768-781 . https://doi.org/10.1021/acscombsci.0c00102