Spatial correlation of desorption events accelerates water exchange dynamics at Pt/water interfaces

Area of Science:

• Physical Chemistry
• Computational Materials Science
• Electrochemistry

Background:

• Water molecule dynamics at metal/water interfaces significantly influence electrochemical processes.
• Accurate simulation of interfacial structure and dynamics is essential for bridging theory and experiments.
• Understanding water exchange dynamics is fundamental for adsorption, desorption, and reaction steps in electrochemistry.

Purpose of the Study:

• To investigate water exchange dynamics at metal/water interfaces using advanced simulation techniques.
• To elucidate the relationship between interfacial solvation structure, hydrogen bonding, and molecular dynamics.
• To provide dynamical insights into electrochemical processes by accurately simulating interfacial water behavior.

Main Methods:

• Employed state-of-the-art machine learning molecular dynamics (MLMD) to balance accuracy and efficiency.
• Reproduced interfacial structures consistent with ab initio molecular dynamics (AIMD) benchmarks.
• Analyzed diffusion, reorientation, and hydrogen bond dynamics of water molecules at the interface.

Main Results:

• Interfacial water exhibits stronger hydrogen bonds compared to bulk water, leading to slower diffusion and reorientation.
• MLMD simulations achieved results in agreement with experimental observations for water dynamics.
• Identified spatial correlation of desorption events, driven by hydrogen bond dynamics, as a key factor accelerating water exchange.

Conclusions:

• Machine learning molecular dynamics (MLMD) offers a powerful approach for studying interfacial water dynamics.
• Stronger interfacial hydrogen bonds modulate water dynamics, while correlated desorption events enhance exchange rates.
• The study advances the understanding of *in situ* interfacial water dynamics, crucial for electrochemical applications.