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Actively learned machine with non-ab initio input features toward efficient CO2 reduction catalyst

Abstract

In conventional chemisorption model, the d-band center theory (augmented sometimes with the upper edge of d-band for imporved accuracy) plays a central role in predicting adsorption energies and catalytic activity as a function of d-band center of the solid surfaces, but it requires density functional calculations that can be quite costly for large scale screening purposes of materials. In this work, we propose to use the d-band width of the muffin-tin orbital theory (to account for local coordination environment) plus electronegativity (to account for adsorbate renormalization) as a simple set of alternative descriptors for chemisorption, which do not demand the ab initio calculations for large-scale first-hand screening purposes. This pair of descriptors are then combined with machine learning methods, namely, artificial neural network (ANN) and kernel ridge regression (KRR), to allow large scale materials screenings. We show, for a toy set of 263 alloy systems, that the CO adsorption energy on the (100) facet can be predicted with a remarkably small mean absolute deviation error of 0.05 eV, a significantly improved result as compared to 0.13 eV obtained with descriptors including costly d-band center calculations in literature. We achieved this high accuracy by utilizing an active learning algorithm, without which the accuracy was 0.18 eV otherwise. In addition, test of the method to other facets such as (111) terrace and (211) step sites suggest that the present model is capable of handling different coordination environments effectively as well. As a practical application of this machine, we identified Cu3Y@Cu* as a highly active and cost-effective electrochemical CO2 reduction catalyst to produce CO with the overpotential ~1 V lower than Au catalyst.

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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, Accepted Manuscript
  • Open access: Creative Commons BY license
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    Actively learned machine with non-ab initio input features toward efficient CO2 reduction catalyst

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

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