# Methodological Frameworks for Computational Electrocatalysis: From Theory to Practice

## TL;DR

This review discusses computational methods for modeling electrocatalytic reactions, focusing on theoretical frameworks and practical considerations for accurate simulations.

## Contribution

The paper provides a comprehensive overview of methodological frameworks for computational electrocatalysis, emphasizing recent machine-learning developments and practical implementation.

## Key findings

- Density functional theory-based methods are central for modeling electrochemical interfaces.
- Machine-learning approaches enable efficient simulations with near-first-principles accuracy.
- Modeling choices significantly affect reliability and computational cost.

## Abstract

Modeling electrocatalytic reactions at solid–liquid interfaces requires capturing both the quantum‐mechanical processes at the electrode surface and the complex response of the surrounding electrochemical environment. This review examines the main theoretical frameworks and computational techniques used to describe such systems, focusing on first‐principles approaches based on density functional theory (DFT). Key aspects include the treatment of reaction thermodynamics, electrode bias, solvation effects, electrolyte screening, and reaction kinetics. A broad range of methods is discussed, from thermochemical models, such as the computational hydrogen electrode, to potential‐dependent formulations based on grand‐canonical DFT and explicit calculation of kinetic barriers. The review also highlights recent machine‐learning approaches for catalyst screening and the growing use of machine‐learning‐based force fields, which promise to enable efficient simulations of complex electrochemical environments over extended time and length scales with near‐first‐principles accuracy. The aim is not only to present the state of the art, but also to clarify the physical assumptions and approximations underlying each approach. The influence of modeling choices on reliability and computational cost is examined in detail. Alongside theoretical aspects, practical considerations are emphasized to support researchers in selecting appropriate methods and designing simulations that are both physically meaningful and computationally tractable.

Computational modeling is widely used to investigate electrocatalytic reactions, yet accurately describing electrochemical interfaces remains challenging. This review outlines theoretical and computational strategies, based on density functional theory, to model reaction thermodynamics, solvation effects, applied bias, and kinetics. Emphasis is placed on methodological assumptions, implementation details, strengths and limitations of each approach, including recent machine‐learning developments.