Understanding Adsorption and Reactions at Aqueous Oxide Interfaces with Neural Network Potential Molecular Dynamics

Area of Science:

• Computational chemistry
• Materials science
• Surface science

Background:

• Chemical processes at metal oxide-water interfaces are crucial for geochemistry, biology, and energy.
• Understanding these interfaces aids in optimizing and controlling chemical reactions.
• Computational modeling is essential due to experimental limitations in complex systems.

Purpose of the Study:

• To review recent efforts in understanding adsorption and reactions at aqueous oxide interfaces using DPMD.
• To highlight the application of deep neural networks (DNNs) for accurate potential energy surface (PES) calculations.
• To provide mechanistic insights into interfacial phenomena relevant to catalysis and material properties.

Main Methods:

• Deep Potential Molecular Dynamics (DPMD) simulations utilizing deep neural networks (DNNs).
• Ab initio calculations to generate training data for machine learning models.
• Analysis of interfacial structure, acid-base chemistry, and adsorption processes.

Main Results:

• DPMD accurately reproduces ab initio PESs for aqueous oxide interfaces.
• Investigated rutile IrO2 interface, showing significant water dissociation and Brønsted acidity.
• Simulations revealed organic acids control TiO2 wettability via acid-base chemistry, not chemisorption.
• Mechanistic insights into methanol's role in enhancing photocatalytic hydrogen evolution on TiO2.

Conclusions:

• DPMD is a powerful tool for simulating complex interfacial phenomena.
• Findings on oxide surface acidity and wettability control have implications for catalysis and self-cleaning surfaces.
• Understanding adsorbate-water interactions at interfaces is key for optimizing photocatalytic processes.