# How Artificial Intelligence Can Advance Electrochemical Science and Identify Water Molecule Orientation on Platinum Electrodes

## TL;DR

This paper explores how artificial intelligence can bridge atomic-level electrochemical insights with macroscopic performance, enabling new discoveries in energy technologies.

## Contribution

The novel contribution is proposing AI as a transformative framework to unify theory, experiment, and data in electrochemical research.

## Key findings

- AI can reveal hidden physical relationships between electrochemical theory and experiment.
- Interpretable AI models could enable closed-loop discovery in electrochemical systems.
- AI integration may accelerate convergence between computational and experimental electrochemistry.

## Abstract

Electrochemistry
lies at the heart of modern energy technologies,
yet connecting atomic-level insights to macroscopic performance remains
an enduring challenge. Quantum-based simulations, such as density
functional theory, have illuminated many fundamental processes, but
their reach is limited by the complexity of real electrochemical environments.
Bridging these scales requires a new conceptual framework that can
expose the hidden connections between theory and experiment. Here,
we argue that the thoughtful integration of artificial intelligence
(AI) can transform electrochemical research by unifying theory, experiment,
and data-driven inference. AI-assisted frameworks can accelerate convergence
between computation and experiment, revealing hidden physical relationships
and enabling closed-loop discovery. Realizing this vision will require
developing transparent, interpretable AI models that earn the same
scientific trust as human reasoning, unlocking deeper understanding
and innovation across the electrochemical sciences.