Jump to main content
Jump to site search

Issue 23, 2018
Previous Article Next Article

Active learning with non-ab initio input features toward efficient CO2 reduction catalysts

Author affiliations

Abstract

In a conventional chemisorption model, the d-band center theory (augmented sometimes with the upper edge of the d-band for improved accuracy) plays a central role in predicting adsorption energies and catalytic activity as a function of the d-band center of solid surfaces, but it requires density functional calculations that can be quite costly for the purposes of large scale screening of materials. In this work, we propose to use the d-band width of the muffin-tin orbital theory (to account for the local coordination environment) plus electronegativity (to account for adsorbate renormalization) as a simple set of alternative descriptors for chemisorption which do not require ab initio calculations for large-scale first-hand screening. This pair of descriptors is then combined with machine learning methods, namely, neural network (NN) and kernel ridge regression (KRR). We show, for a toy set of 263 alloy systems, that the CO adsorption energy on the (100) facet can be predicted with a mean absolute deviation error of 0.05 eV. We achieved this high accuracy by utilizing an active learning algorithm, without which the accuracy was 0.18 eV. In addition, the results of testing the method with other facets such as (111) terrace and (211) step sites suggest that the present model is also capable of handling different coordination environments effectively. As an example of the practical application of this machine, we identified Cu3Y@Cu* as an active and cost-effective electrochemical CO2 reduction catalyst to produce CO with an overpotential ∼1 V lower than a Au catalyst.

Graphical abstract: Active learning with non-ab initio input features toward efficient CO2 reduction catalysts

Back to tab navigation

Supplementary files

Publication details

The article was received on 06 Aug 2017, accepted on 16 Apr 2018 and first published on 17 Apr 2018


Article type: Edge Article
DOI: 10.1039/C7SC03422A
Citation: Chem. Sci., 2018,9, 5152-5159
  • Open access: Creative Commons BY license
  •   Request permissions

    Active learning with non-ab initio input features toward efficient CO2 reduction catalysts

    J. Noh, S. Back, J. Kim and Y. Jung, Chem. Sci., 2018, 9, 5152
    DOI: 10.1039/C7SC03422A

    This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. Material from this article can be used in other publications provided that the correct acknowledgement is given with the reproduced material.

    Reproduced material should be attributed as follows:

    • For reproduction of material from NJC:
      [Original citation] - Published by The Royal Society of Chemistry (RSC) on behalf of the Centre National de la Recherche Scientifique (CNRS) and the RSC.
    • For reproduction of material from PCCP:
      [Original citation] - Published by the PCCP Owner Societies.
    • For reproduction of material from PPS:
      [Original citation] - Published by The Royal Society of Chemistry (RSC) on behalf of the European Society for Photobiology, the European Photochemistry Association, and RSC.
    • For reproduction of material from all other RSC journals:
      [Original citation] - Published by The Royal Society of Chemistry.

    Information about reproducing material from RSC articles with different licences is available on our Permission Requests page.

Search articles by author

Spotlight

Advertisements