Band structure

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Band structure#

We can compute the bandstructure from trained model with simply a few line of code or command. Here is an example:

Band api#

Import the necessary package

from dptb.nn.build import build_model
from dptb.postprocess.import Band

build the model and band class

model = build_model("your model path")
band = Band(model=model, device=model.device)

Then, we can call band.get_bands() to compute the band structure, which takes a few variables as input:

band.get_bands(
    data: Union[AtomicData, ase.Atoms, str], 
    kpath_kwargs: dict, 
    AtomicData_options: dict={}
):

Here data can be an AtomicData class(see quick_start/basic_API for usage), an ase.Atoms class, or any atomic structure files format path that is readable by ase.io.read.

If using ase.Atoms class or string of structure file path, we need to set the AtomicData_options, which is a dict contrains the basis definition of the atomic graph. a template looks like:

AtomicData_options = {
    "r_max": 5.0,
    "er_max": 5.0, # optional, needed if using neural network env correction
    "oer_max": 5.0 # optional, needed if using onsite strain correction
    "pbc": true # optional, default true
}

Now what is needed is the kpath_kwargs, which defines the kpoint setting in plotting the bandstructure. We support different types of kpath definition, includes ase/abacus/vasp/array. Here we introduce the ase and abacus mode here:

# ase
kpath_kwargs = {
    "kline_type" = "ase",
    "kpath" = "GMKG",
    "nkpoint" = 300,
}

# abacus
kpath_kwargs = {
    "kline_type" = "abacus",
    "kpath" = [
            [0, 0, 0, 30],
            [0.5, 0, 0, 30],
            [0.3333333, 0.3333333, 0, 30],
            [0, 0, 0, 1]
        ],
    "klabels" = ["G", "M", "K", "G"]
}
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