Bond Density Evaluation and Crystallographic Plane Exfoliation
This repository provides computational methods to identify crystallographic planes suitable for exfoliation from non-van der Waals (non-vdW) bulk materials by evaluating bond density and performing systematic structural exfoliation simulations.
Bond Density Evaluation and Plane Selection
Efficiently screen and identify exfoliable crystallographic planes through the following workflow:
1. Acquiring XRD Patterns
Note: Combine experimental data from the Materials Project or ICSD database with calculated peak positions to identify matches for selection.
1 | python simulate_xrd.py |
2. Identify Crystallographic Planes
1 | python identify_planes.py |
3. Bond Density Analysis
1 | python bond_density_scan.py |
Planes exhibiting relatively low bond density are promising for exfoliation.
4. Calculating Anisotropic Binding Strength
For those crystallographic plane with low bond density. Use common density functional theory (DFT) software to calculate bonding strengths and determine the differences between out-of-plane and in-plane bonding.
The definition of Anisotropic Binding Strength is as follows: The Binding Strength calculated by DFT software is used to assess the in-plane and out-of-plane bonds of the respective crystal plane. The difference between the Binding Strength of the out-of-plane bonds and in-plane bonds is then divided by the surface area to obtain the Anisotropic Binding Strength.
Step-wise Exfoliation Procedure
Note: DFT-D3 van der Waals corrections are integrated into all exfoliation simulations.
Follow these detailed steps to simulate the exfoliation process:
1. Align Structure
Rotate the crystal structure to align the targeted exfoliation plane perpendicular to the c-axis.
2. Stretch and Relax Structure
Incrementally stretch the lattice normal to the exfoliation plane, moving the outermost atomic layer approximately Δd = 0.1~0.2 Å per step, followed by structural relaxation at each increment.
Dependencies
- Python version: 3.9–3.12
- Required Python libraries:
numpy,scipy,matplotlib,pymatgen,ase
Install dependencies via:
1 | pip install numpy scipy matplotlib pymatgen ase |
References
The methodologies used incorporate established computational techniques, including DFT-D3 for energy calculations. Please ensure proper referencing when using these methodologies in your publications.
License
This project is licensed under the MIT License.