Predicting the Charge Density Response in Metal Electrodes

Abstract

The computational study of energy storage and conversion processes calls for simulation techniques that can reproduce the electronic response of metal electrodes under electric fields. Despite recent advancements in machine-learning methods applied to electronic-structure properties, predicting the non-local behavior of the charge density in electronic conductors remains a major open challenge. We combine long-range and equivariant kernel methods to predict the Kohn-Sham electron density of metal electrodes in response to various kinds of electric field perturbations. By taking slabs of gold as an example, we first show how the non-local electronic polarization generated by the interaction with an ionic species can be accurately reproduced in electrodes of arbitrary thickness. A finite-field extension of the method is then introduced, which allows us to predict the charge transfer and the electrostatic potential drop induced by the application of a homogeneous and constant electric field. Finally, we demonstrate the capability of the method to reproduce the charge-density response in a gold/electrolyte capacitor under an applied voltage, predicting the system polarization with a greater accuracy than state-of-the-art classical atomic-charge models.

arXiv comment6 pages, 3 figures

References in corpus (29)

• Separable Dual Space Gaussian Pseudo-potentials
• CP2K: An Electronic Structure and Molecular Dynamics Software Package -- Quickstep: Efficient and Accurate Electronic Structure Calculations
• On representing chemical environments
• By-passing the Kohn-Sham equations with machine learning
• A Fourth-Generation High-Dimensional Neural Network Potential with Accurate Electrostatics Including Non-local Charge Transfer
• Physics-inspired structural representations for molecules and materials
• Joint Density-Functional Theory of the Electrode-Electrolyte Interface: Application to Fixed Electrode Potentials, Interfacial Capacitances, and Potentials of Zero Charge
• Symmetry-Adapted Machine-Learning for Tensorial Properties of Atomistic Systems
• Interatomic potentials for ionic systems with density functional accuracy based on charge densities obtained by a neural network
• Automatic Selection of Atomic Fingerprints and Reference Configurations for Machine-Learning Potentials
• A Transferable Machine-Learning Model of the Electron Density
• A deep potential model with long-range electrostatic interactions
• Incorporating long-range physics in atomic-scale machine learning
• Atom-Density Representations for Machine Learning
• Charge fluctuations in nano-scale capacitors
• Self-Consistent Determination of Long-Range Electrostatics in Neural Network Potentials
• Feature Optimization for Atomistic Machine Learning Yields A Data-Driven Construction of the Periodic Table of the Elements
• Charge fluctuations from molecular simulations in the constant-potential ensemble
• Simulating electrochemical systems by combining the finite field method with a constant potential electrode
• A semiclassical Thomas-Fermi model to tune the metallicity of electrodes in molecular simulations
• Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
• Constrained-DFT method for accurate energy level alignment of metal/molecule interfaces
• Ab-initio theory of metal-insulator interfaces in a finite electric field
• Microscopic origin of the effect of substrate metallicity on interfacial free energies
• PiNNwall: Heterogeneous Electrode Models from Integrating Machine Learning and Atomistic Simulation
• Constant Chemical Potential-Quantum Mechanical-Molecular Dynamics simulations of the Graphene-electrolyte double layer
• Predicting the Electronic Density Response of Condensed-Phase Systems to Electric Field Perturbations
• Three Dimensional Activity Volcano Plot under External Electric Field
• Impact of quantum-chemical metrics on the machine learning prediction of electron density

Cited by in corpus (5)

• Machine Learning Potential for Electrochemical Interfaces with Hybrid Representation of Dielectric Response
• Accelerating QM/MM simulations of electrochemical interfaces through machine learning of electronic charge densities
• Predicting the Electronic Density Response of Condensed-Phase Systems to Electric Field Perturbations
• Learning the Electrostatic Response of the Electron Density through a Symmetry-Adapted Vector Field Model
• Brownian dynamics simulations of electric double-layer capacitors with tunable metallicity