Macro-Dipole-Constrainted Learning of Atomic Charges for Accurate Electrostatic Potentials at Electrochemical Interfaces

Abstract

Large thermal fluctuations of the liquid phase obscure the weak macroscopic electric field that drives electrochemical reactions, rendering the extraction of reliable interfacial charge distributions from ab initio molecular dynamics extremely challenging. We introduce SMILE-CP (Scalar Macro-dipole Integrated LEarning - Charge Partitioning), a macro-dipole-constrained scheme that infers atomic charges using only the instantaneous atomic coordinates and the total dipole moment of the simulation cell - quantities routinely available from standard density-functional theory calculations. SMILE-CP preserves both the global electrostatic field and the local potential without invoking any explicit charge-partitioning scheme. Benchmarks on three representative electrochemical interfaces - nanoconfined water, Mg2+ dissolution in water, and a kinked Mg vicinal surface under anodic bias - show that SMILE-CP eliminates the qualitative errors observed for unconstrained charge decompositions. The method is computationally inexpensive and data-efficient, opening the door to charge-aware machine-learning potentials capable of bias-controlled, nanosecond-scale simulations of realistic electrochemical systems.

6 pages, 4 figures

References in corpus (19)

• Wannier90: A Tool for Obtaining Maximally-Localised Wannier Functions
• Maximally localized Wannier functions: Theory and applications
• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges
• A Performance and Cost Assessment of Machine Learning Interatomic Potentials
• DScribe: Library of Descriptors for Machine Learning in Materials Science
• Machine-learning based interatomic potential for amorphous carbon
• A Fourth-Generation High-Dimensional Neural Network Potential with Accurate Electrostatics Including Non-local Charge Transfer
• A deep potential model with long-range electrostatic interactions
• Incorporating long-range physics in atomic-scale machine learning
• Deep neural network for the dielectric response of insulators
• Self-Consistent Determination of Long-Range Electrostatics in Neural Network Potentials
• Dielectric properties of nano-confined water: a canonical thermopotentiostat approach
• Updates to the DScribe Library: New Descriptors and Derivatives
• Effect of the metallicity on the capacitance of gold - aqueous sodium chloride interfaces
• Molecular dynamics simulation with finite electric fields using Perturbed Neural Network Potentials
• Machine Learning Potential for Electrochemical Interfaces with Hybrid Representation of Dielectric Response
• On the origin of univalent Mg${}^{+}$ ions in solution and their role in anomalous anodic hydrogen evolution
• Iterative charge equilibration for fourth-generation high-dimensional neural network potentials