Diffusion, mechanical and thermal properties of sT hydrogen hydrate by machine learning potential

Area of Science:

• Materials Science
• Computational Chemistry
• Energy Storage

Background:

• Structure T (sT) hydrate is a novel material with potential applications in hydrogen storage and transport.
• The fundamental properties of sT hydrate are not yet well understood, hindering its practical application.

Purpose of the Study:

• To develop a machine learning potential (MLP) for sT hydrogen hydrate.
• To comprehensively investigate the structural, mechanical, thermal, and hydrogen diffusion properties of sT hydrate using the developed MLP.

Main Methods:

• Quantum-mechanical molecular dynamics simulations were used to generate data for training.
• A machine learning potential (MLP) forcefield was developed based on the simulation data.
• Extensive simulations using the MLP were performed to explore material properties.

Main Results:

• The MLP accurately reproduces structural, mechanical, and thermal properties compared to DFT and empirical forcefields.
• Hydrogen molecule mobility in sT hydrate is restricted by the material's unique cavities and narrow pore windows.
• sT hydrate exhibits brittle failure under uniaxial tension, characterized by an initial stress increase followed by a sharp drop.
• Thermal conductivity is primarily governed by the host water molecules' hydrogen-bonded network, with guest hydrogen molecules having a negligible impact.

Conclusions:

• The developed MLP provides a reliable tool for studying sT hydrogen hydrate properties.
• sT hydrate's structural features significantly impede hydrogen diffusion.
• The material's mechanical behavior is brittle, and its thermal transport is dominated by the water framework.