Accelerating QM/MM simulations of electrochemical interfaces through machine learning of electronic charge densities - Institut des Sciences du Calcul et des Données Accéder directement au contenu
Article Dans Une Revue The Journal of Chemical Physics Année : 2024

Accelerating QM/MM simulations of electrochemical interfaces through machine learning of electronic charge densities

Résumé

A crucial aspect in the simulation of electrochemical interfaces consists in treating the distribution of electronic charge of electrode materials that are put in contact with an electrolyte solution. Recently, it has been shown how a machine-learning method that specifically targets the electronic charge density, also known as SALTED, can be used to predict the long-range response of metal electrodes in model electrochemical cells. In this work, we provide a full integration of SALTED with MetalWalls, a program for performing classical simulations of electrochemical systems. We do so by deriving a spherical harmonics extension of the Ewald summation method, which allows us to efficiently compute the electric field originated by the predicted electrode charge distribution. We show how to use this method to drive the molecular dynamics of an aqueous electrolyte solution under the quantum electric field of a gold electrode, which is matched to the accuracy of density-functional theory. Notably, we find that the resulting atomic forces present a small error of the order of 1 meV/Å, demonstrating the great effectiveness of adopting an electron-density path in predicting the electrostatics of the system. Upon running the data-driven dynamics over about 3 ns, we observe qualitative differences in the interfacial distribution of the electrolyte with respect to the results of a classical simulation. By greatly accelerating quantum-mechanics/molecular-mechanics approaches applied to electrochemical systems, our method opens the door to nanosecond timescales in the accurate atomistic description of the electrical double layer.
Fichier principal
Vignette du fichier
JCP24-AR-02019.pdf (810.4 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04643797 , version 1 (10-07-2024)

Licence

Copyright (Tous droits réservés)

Identifiants

Citer

Andrea Grisafi, Mathieu Salanne. Accelerating QM/MM simulations of electrochemical interfaces through machine learning of electronic charge densities. The Journal of Chemical Physics, 2024, 161 (2), pp.024109. ⟨10.1063/5.0218379⟩. ⟨hal-04643797⟩
0 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More