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Neural network potentials for accelerated metadynamics of oxygen reduction kinetics at Au–water interfaces
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society of Chemistry
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10074416/ https://www.ncbi.nlm.nih.gov/pubmed/37035698 http://dx.doi.org/10.1039/d2sc06696c |
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author | Yang, Xin Bhowmik, Arghya Vegge, Tejs Hansen, Heine Anton |
author_facet | Yang, Xin Bhowmik, Arghya Vegge, Tejs Hansen, Heine Anton |
author_sort | Yang, Xin |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10074416 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Royal Society of Chemistry |
record_format | MEDLINE/PubMed |
spelling | pubmed-100744162023-04-06 Neural network potentials for accelerated metadynamics of oxygen reduction kinetics at Au–water interfaces Yang, Xin Bhowmik, Arghya Vegge, Tejs Hansen, Heine Anton Chem Sci Chemistry 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. The Royal Society of Chemistry 2023-03-13 /pmc/articles/PMC10074416/ /pubmed/37035698 http://dx.doi.org/10.1039/d2sc06696c Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by/3.0/ |
spellingShingle | Chemistry Yang, Xin Bhowmik, Arghya Vegge, Tejs Hansen, Heine Anton Neural network potentials for accelerated metadynamics of oxygen reduction kinetics at Au–water interfaces |
title | Neural network potentials for accelerated metadynamics of oxygen reduction kinetics at Au–water interfaces |
title_full | Neural network potentials for accelerated metadynamics of oxygen reduction kinetics at Au–water interfaces |
title_fullStr | Neural network potentials for accelerated metadynamics of oxygen reduction kinetics at Au–water interfaces |
title_full_unstemmed | Neural network potentials for accelerated metadynamics of oxygen reduction kinetics at Au–water interfaces |
title_short | Neural network potentials for accelerated metadynamics of oxygen reduction kinetics at Au–water interfaces |
title_sort | neural network potentials for accelerated metadynamics of oxygen reduction kinetics at au–water interfaces |
topic | Chemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10074416/ https://www.ncbi.nlm.nih.gov/pubmed/37035698 http://dx.doi.org/10.1039/d2sc06696c |
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