Title: Catalyst design using actively learned machine with non-ab initio input features towards CO2 reduction reactions,
Abstract: In conventional chemisorption model, the d-band center theory (augmented
sometimes with the upper edge of d-band for imporved accuarcy) 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. 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 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. 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 0.37 V lower than Au catalyst.