Accelerated Percolation Path Identification in Twisted Bilayer Graphene

Area of Science:

• Computational Materials Science
• Condensed Matter Physics
• Electrochemistry

Background:

• Twisted bilayer graphene is a promising material for energy storage applications.
• Efficient ion transport is critical for the performance of battery anode materials.
• Identifying ion diffusion pathways is computationally intensive.

Purpose of the Study:

• To present a novel computational workflow for accelerating the identification of percolation pathways in twisted bilayer graphene.
• To investigate the influence of twist angle on Li-ion diffusion in twisted bilayer graphene.
• To establish a general and efficient method for mapping percolation paths in materials.

Main Methods:

• Utilized charge density from a single ab initio Density Functional Theory calculation.
• Developed a three-step workflow: intercalation site identification, migration graph generation, and path-finding algorithm.
• Applied the workflow to Li-diffusion in 21 twist-angle structures of twisted bilayer graphene.

Main Results:

• Successfully identified physically plausible percolation pathways for Li-diffusion in all tested twisted bilayer graphene structures.
• Discovered a significant relationship between the twist angle and the ease of ion percolation.
• Demonstrated the computational workflow's speed and generality compared to conventional methods.

Conclusions:

• The novel computational workflow efficiently maps ion percolation pathways in twisted bilayer graphene.
• Twist angle is a critical parameter influencing ion diffusion kinetics in these materials.
• The method facilitates rapid exploration of ion transport in diverse 2D and 3D materials for energy applications.