Efficient Machine-Learning-Aided Screening of Hydrogen Adsorption on Bimetallic Nanoclusters.

Adam S. Foster,Eiaki V. Morooka,Yashasvi S. Ranawat,Marc O. J. Jäger,Filippo Federici Canova

doi:10.1021/acscombsci.0c00102

Adam S. Foster, Eiaki V. Morooka

Open Access

https://doi.org/10.1021/acscombsci.0c00102

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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.

Highlights

Excess electricity in the grid can be stored in a long-term energy carrier such as hydrogen, via electrolysis of water.[1,2] The energy is released either directly back into the electric grid at times of high demand, in fuel cell vehicles, or through the gas grid for heating.[2−4] the production of hydrogen through electrolysis is generally more expensive than from natural gas, oil, or coal, that means it is not a competitive energy carrier yet.[2]
Since the electricity accounts for 70−90% of the cost, the situation can change in the future when more volatile electricity prices are expected.[5−7] Water splits electrolytically into hydrogen at the cathode via the hydrogen evolution reaction (HER) and oxygen at the anode via the oxygen evolution reaction (OER).[8−10] Whereas OER catalysts are mostly transition metals in oxidized states,[11] HER catalysts normally contain reduced transition metals and platinum is often used commercially.[8]
In order to reduce prices, the platinum content has been minimized in the past decade, but catalyst loading still makes up a significant portion of the proton-exchange membrane (PEM) electrolyzer cost.[12]

Summary

Introduction

Excess electricity in the grid can be stored in a long-term energy carrier such as hydrogen, via electrolysis of water.[1,2] The energy is released either directly back into the electric grid at times of high demand, in fuel cell vehicles, or through the gas grid for heating.[2−4] the production of hydrogen through electrolysis is generally more expensive than from natural gas, oil, or coal, that means it is not a competitive energy carrier yet.[2]. Its reduction or replacement remains a key component to producing hydrogen via electrolysis competitively.[14]

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