Dataset for "Machine Learning Stability and Bandgaps of Lead-Free Perovskites for Photovoltaics"

Datasets used in the publication "Machine Learning Stability and Bandgaps of Lead-Free Perovskites for Photovoltaics"  [doi:10.1002/adts.201900178].

All structures were relaxed with the following parameters using Quantumwise QATK 2017:

- SG15-GGA norm-conserving (Vanderbilt) pseudopotentials employed in a LCAO-approach (200 Hartree cutoff)
- 2x1x2-cubic-perovskite-supercells, relaxed from cubic 11.4Åx5.7Åx11.4Å-structures (forces < 0.01eV/Å)
- 300K Fermi-Dirac-smearing
- a 6x12x6 k-point grid (Monkhorst-Pack)

Specifically, the included files are:

db_2.data: the actual database used for model building (json-format)
lead_set.data: the "external" test set used to test predictive power with out of sample compounds (json-format)
load_stanley_c.py: a python script to parse the .json-files to a python-dictionary including the structures (relaxed and unrelaxed) as ASE-atoms

The format of the datafiles is as follows (-1 generally denote values not parsed from the raw data):
{
    "<idstring>" : {
        "trajectory" : n/a,
        "energy" : total DFT energy in eV,
        "rstruc" : relaxed structure, 3-tuple: (cell-vectors, scaled_positions, elements),
        "gaps" : { "opt_gap", "ind_gap } - both direct and indirect gap,
        "effective_mass" : n/a,
        "iterations" : number of relaxation steps,
         "calc" : some calculation metadata,
        "ustruc" : unrelaxed input structure,
    
    }
}
Missing ids relate to structures filtered out, because the calculation didn't converge.

Some code which works with a different representation of this data can be found at  https://github.com/jstanai/Machine-Learning-Perovskite-Properties-for-Photovoltaics