gridmlip 0.2

Grid-based method for calculating the percolation barrier of mobile species using machine learning interatomic potentials

gridmlip

Contents

• About
• Installation
• How to use
• Notebooks
• How to cite

About

gridmlip is a library for calculating percolation barriers of mobile species in solids using grid-based method with machine learning interatomic potentials (MLIPs).

Installation

pip install gridmlip

or

git clone https://github.com/dembart/gridmlip
cd gridmlip
pip install .

How to use

Here we describe the pipeline in general. For a specific example, see Notebooks.

Step #1: Construct configurations for processing with your favorite MLIP

from gridmlip import Grid

atomic_types_mapper = {3:0, 31:1, 17:2}
grid = Grid.from_file('your.cif', specie = 3, r_min = 1.8, 
                    atomic_types_mapper=atomic_types_mapper # optional
                  )
cfgs = grid.construct_configurations('data.cfg')

Step #2: Evaluate the configurations with your favorite MLIP

mlp calculate_efs p.mtp data.cfg --output_filename=processed_data.cfg'

Step #3: Read processed configrations and calculate the percolation barriers

from gridmlip import Grid

grid = Grid.from_file('your.cif', specie = 3, r_min = 1.8)
grid.read_processed_configurations('processed_data.cfg', format = 'cfg')
barriers = grid.percolation_barriers()

Step #4: Write .grd or .cube file for visualization in VESTA 3.0

g.write_grd('test.grd')

Notebooks

• Using TensorNet model pre-trained on MatPES
  

How to cite

If you use the gridmlip library, please, consider citing this repository.