Revisiting Trends in the Exchange Current for Hydrogen Evolution

TL;DR

This paper improves the kinetic model for hydrogen evolution on transition metals by incorporating metal-dependent rate constants, significantly enhancing the accuracy of exchange current predictions and demonstrating the benefit of machine learning integration.

Contribution

The study introduces a metal-dependent rate constant into the kinetic model, significantly reducing prediction errors and applying machine learning to further refine the model.

Findings

Enhanced model reduces discrepancy by up to four orders of magnitude.

Logarithm of rate constant linearly depends on hydrogen adsorption free energy.

Machine learning further improves model accuracy.

Abstract

N{\o}rskov and collaborators proposed a simple kinetic model to explain the volcano relation for the hydrogen evolution reaction on transition metal surfaces in such that $ j_0= k_0 f({\Delta}G_H)$ where j_0 is the exchange current density, $f({\Delta}G_H)$ is a function of the hydrogen adsorption free energy ${\Delta}G_H$ as computed from density functional theory, and $k_0$ is a universal rate constant. Herein, focusing on the hydrogen evolution reaction in acidic medium, we revisit the original experimental data and find that the fidelity of this kinetic model can be significantly improved by invoking metal-dependence on $k_0$ such that the logarithm of $k_0$ linearly depends on the absolute value of ${\Delta}G_H$. We further confirm this relationship using additional experimental data points obtained from a critical review of the available literature. Our analyses show that the new model decreases the discrepancy between calculated and experimental exchange current density values by up to four orders of magnitude. Furthermore, we show the model can be further improved using machine learning and statistical inference methods that integrate additional material properties