This package provides tools for randomly generating low dimensional hybrid organic inorganic perovskites, as used in https://pubs.acs.org/doi/10.1021/jacs.4c06549.

The simplest intended use is to create random 2D hybrid perovskite structures, given an organic cation, a choice of halide, and some details about the unit cell.

First create an inorganic monolayer

from LDHPbuilder.perovskite_builder import OrganicMolecule, InorganicMonolayer, PerovskiteBuilder

monolayer = InorganicMonolayer.from_species_specification('Pb', 'Br', num_unit_cell_octahedra=2)

num_unit_cell_octahedra is the number of lead atoms in each monolayer, in each unit cell. The InorganicMonolayer computes some useful properties when given an atoms object representing a monolayer. It also provides the useful class method from_species_specification.

Then construct and OrganicMolecule from an ase Atoms object.

molecule = OrganicMolecule(
    atoms,
    1. # charge
)

Random structures are then created:

pb = PerovskiteBuilder()

samples = pb.generate_homogeneous_perovskite_samples( # homogeneous since just one kind of molecule
    monolayer, 
    molecule, 
    num_layers=2, # should be a power of 2, but 4 already implies a very large search task
    num_samples=10, 
    max_num_attempts=15, 
    stacking_method='total_thickness' # this can be either 'total_thickness' or 'half_thickness'. use half_thickness for long, thin +1 molecules
)

Key functions are documented through docstrings.

This code was introduced in the following paper:

@article{KarimitariBaldwin2024,
    author = {Karimitari, Nima and Baldwin, William J. and Muller, Evan W. and Bare, Zachary J. L. and Kennedy, W. Joshua and Csányi, Gábor and Sutton, Christopher},
    title = {Accurate Crystal Structure Prediction of New 2D Hybrid Organic–Inorganic Perovskites},
    journal = {Journal of the American Chemical Society},
    volume = {146},
    number = {40},
    pages = {27392-27404},
    year = {2024},
    doi = {10.1021/jacs.4c06549},
    note ={PMID: 39344597},
    URL = {https://doi.org/10.1021/jacs.4c06549}
}

If you want to contribute to, or use our package for a scientific application, please feel free to contact us via the emails in the citation.

There are many extensions which could be easily added, please let us know via an issue.

As well as ase, numpy, scipy, We use aseMolec for working with ase.Atoms objects representing organic molecules. https://github.com/imagdau/aseMolec.