What question did this study set out to answer?

The aim is to model metal–electrolyte interfaces accurately using machine learning interatomic potentials.

April 15, 2026ACS electrochemistry.

Estimating Potential-Dependent Physicochemical Properties at Metal–Electrolyte Interfaces Using Machine Learning Interatomic Potentials

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Authors

AMAnkit MathankerLawrence Livermore National Laboratory JGJiawei GuoLawrence Livermore National Laboratory BGBryan R. Goldsmith

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Implication

This work demonstrates machine learning models predict physicochemical properties in metal–electrolyte systems, suggesting improved design for electrochemical applications.

Key Points

The aim is to model metal–electrolyte interfaces accurately using machine learning interatomic potentials.
Developed machine learning interatomic potentials (MLIPs) using the MACE architecture.
Applied MLIPs within an active learning workflow for aqueous NaCl electrolytes.
Conducted MD simulations with MLIPs across various metal surfaces (Au, Cu, Rh).
Achieved DFT-level accuracy across 21 different metal–electrolyte systems.
Identified a universal trend of water and ion coverage in relation to surface charge density.
Observed distinct capacitance regimes for metal electrodes before and after the pzc.
Highlighted stronger interactions of Cl– ions with metal surfaces compared to Na+.

Cite This Study

Mathanker et al. (2026) studied this question.

synapsesocial.com/papers/69df2c62e4eeef8a2a6b182f https://doi.org/10.1021/acselectrochem.5c00540

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Implication

This work demonstrates machine learning models predict physicochemical properties in metal–electrolyte systems, suggesting improved design for electrochemical applications.

Estimating Potential-Dependent Physicochemical Properties at Metal–Electrolyte Interfaces Using Machine Learning Interatomic Potentials

Discussion

Member takes

Implication

Key Points

Cite This Study

Estimating Potential-Dependent Physicochemical Properties at Metal–Electrolyte Interfaces Using Machine Learning Interatomic Potentials

Discussion

Member takes

Implication

Key Points

Cite This Study

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