Issue 14, 2023

Neural network potentials for accelerated metadynamics of oxygen reduction kinetics at Au–water interfaces

Abstract

The application of ab initio molecular dynamics (AIMD) for the explicit modeling of reactions at solid–liquid interfaces in electrochemical energy conversion systems like batteries and fuel cells can provide new understandings towards reaction mechanisms. However, its prohibitive computational cost severely restricts the time- and length-scales of AIMD. Equivariant graph neural network (GNN) based accurate surrogate potentials can accelerate the speed of performing molecular dynamics after learning on representative structures in a data efficient manner. In this study, we combined uncertainty-aware GNN potentials and enhanced sampling to investigate the reactive process of the oxygen reduction reaction (ORR) at an Au(100)–water interface. By using a well-established active learning framework based on CUR matrix decomposition, we can evenly sample equilibrium structures from MD simulations and non-equilibrium reaction intermediates that are rarely visited during the reaction. The trained GNNs have shown exceptional performance in terms of force prediction accuracy, the ability to reproduce structural properties, and low uncertainties when performing MD and metadynamics simulations. Furthermore, the collective variables employed in this work enabled the automatic search of reaction pathways and provide a detailed understanding towards the ORR reaction mechanism on Au(100). Our simulations identified the associative reaction mechanism without the presence of *O and a low reaction barrier of 0.3 eV, which is in agreement with experimental findings. The methodology employed in this study can pave the way for modeling complex chemical reactions at electrochemical interfaces with an explicit solvent under ambient conditions.

Graphical abstract: Neural network potentials for accelerated metadynamics of oxygen reduction kinetics at Au–water interfaces

Supplementary files

Article information

Article type
Edge Article
Submitted
05 Dec 2022
Accepted
09 Mar 2023
First published
13 Mar 2023
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2023,14, 3913-3922

Neural network potentials for accelerated metadynamics of oxygen reduction kinetics at Au–water interfaces

X. Yang, A. Bhowmik, T. Vegge and H. A. Hansen, Chem. Sci., 2023, 14, 3913 DOI: 10.1039/D2SC06696C

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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