Catalyst design using actively learned machine with non-ab initio input features towards CO2 reduction reactions

09/14/2017
by   Juhwan Noh, et al.
0

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.

READ FULL TEXT
research
01/04/2023

Machine-Learning Prediction of the Computed Band Gaps of Double Perovskite Materials

Prediction of the electronic structure of functional materials is essent...
research
10/30/2018

Band gap prediction for large organic crystal structures with machine learning

Machine learning models are capable of capturing the structure-property ...
research
09/12/2023

Band-gap regression with architecture-optimized message-passing neural networks

Graph-based neural networks and, specifically, message-passing neural ne...
research
06/02/2020

Committee neural network potentials control generalization errors and enable active learning

It is well known in the field of machine learning that committee models ...
research
05/03/2023

Shotgun crystal structure prediction using machine-learned formation energies

Stable or metastable crystal structures of assembled atoms can be predic...
research
06/21/2020

Learning the electronic density of states in condensed matter

The electronic density of states (DOS) quantifies the distribution of th...

Please sign up or login with your details

Forgot password? Click here to reset