def run(**kwargs):
"""
Run AMSET on an ab initio band structure
"""
from amset.constants import defaults
from amset.core.run import Runner
from amset.log import initialize_amset_logger
if kwargs["print_log"] is not False:
initialize_amset_logger()
settings_override: Dict[str, Dict[str, Any]] = defaultdict(dict)
for setting in defaults:
if setting in kwargs and kwargs[setting] is not None:
settings_override[setting] = kwargs[setting]
runner = Runner.from_directory(
directory=kwargs["directory"],
vasprun=kwargs["vasprun"],
settings_file=kwargs["settings"],
settings_override=settings_override,
)
runner.run()
def test_run_amset_from_vasprun(
example_dir, system, settings, transport, max_aniso, files, scats
):
vasprun, settings = _prep_inputs(example_dir, system, settings)
runner = Runner.from_vasprun(vasprun, settings)
amset_data = runner.run()
_validate_data(amset_data, transport, max_aniso, files, scats)
def test_run_amset_from_directory(
example_dir, system, settings, transport, max_aniso, files, scats
):
vasprun, settings = _prep_inputs(example_dir, system, settings)
dumpfn(settings, "settings.yaml")
runner = Runner.from_directory(".", input_file=vasprun, settings_override=settings)
amset_data = runner.run()
_validate_data(amset_data, transport, max_aniso, files, scats)
def test_run_tricky_spin_polarized(band_structure_data):
settings = {
"interpolation_factor": 2,
"temperatures": [300],
"doping": [1e15],
"deformation_potential": (6.5, 6.5),
"elastic_constant": 190,
"use_projections": True,
"scattering_type": ["ADP"],
"nworkers": 1,
}
files = ["transport", "!mesh"]
scats = ["ADP"]
transport = {
("mobility", ("overall", (0, 0))): 1.261213643946453,
("seebeck", (0, 0)): 1356.1341462605026,
}
bs = band_structure_data["tricky_sp"]["band_structure"]
nelect = band_structure_data["tricky_sp"]["nelect"]
runner = Runner(bs, nelect, settings)
amset_data = runner.run()
_validate_data(amset_data, transport, 1, files, scats)
# general settings
"doping": [-3e13],
"temperatures": [201, 290, 401, 506, 605, 789, 994],
"bandgap": 1.33,
# electronic_structure settings
"interpolation_factor": 50,
# scattering rate settings
"deformation_potential": (1.2, 8.6),
"elastic_constant": 139.7,
"donor_charge": 1,
"acceptor_charge": 1,
"static_dielectric": 12.18,
"high_frequency_dielectric": 10.32,
"pop_frequency": 8.16,
# performance settings
"mobility_rates_only": True,
"write_mesh": True,
}
if __name__ == "__main__":
runner = Runner.from_vasprun("vasprun.xml.gz", settings)
amset_data = runner.run()
plotter = RatesPlotter(amset_data)
plt = plotter.get_plot()
plt.savefig("GaAs_rates.png", bbox_inches="tight", dpi=400)
def eff_mass(filename, **kwargs):
"""
Calculate conductivity effective mass.
"""
import numpy as np
from monty.serialization import loadfn
from scipy import constants
from tabulate import tabulate
from amset.constants import bohr_to_cm, defaults
from amset.core.run import Runner
settings = {
"scattering_type": ["CRT"],
"constant_relaxation_time": 1e-14,
"use_projections": True,
"calculate_mobility": False,
"separate_mobility": False,
"write_log": False,
"file_format": None,
}
for setting in defaults:
if setting in kwargs and kwargs[setting] is not None:
settings[setting] = kwargs[setting]
elif setting == "doping" or setting == "temperatures":
settings[setting] = temp_doping_defaults[setting]
if "json" in filename:
data = loadfn(filename)
runner = Runner(data["band_structure"], data["nelect"], settings)
else:
runner = Runner.from_vasprun(filename, settings)
amset_data = runner.run()
inv_cond = np.linalg.inv(amset_data.conductivity)
doping_scale = 1 / bohr_to_cm**3
doping = (np.abs(amset_data.doping) * doping_scale)[:, None, None, None]
crt = settings["constant_relaxation_time"]
masses = inv_cond * crt * doping * 10**6 * constants.e**2 / constants.m_e
mass_info = []
for n, t in np.ndindex(amset_data.fermi_levels.shape):
info = [
amset_data.doping[n] * doping_scale, amset_data.temperatures[t]
]
if kwargs["average"]:
eigs = np.linalg.eigvals(masses).real
info.append(3 / (1 / eigs[n, t]).sum())
else:
xyz = np.diagonal(masses, axis1=-2, axis2=-1)
info.extend(xyz[n, t].tolist())
mass_info.append(info)
headers = ["conc [cm⁻³]", "temp [K]"]
floatfmt = [".2e", ".1f"]
if kwargs["average"]:
headers.append("m*")
floatfmt.append(".3f")
else:
headers.extend(["mₓₓ*", "mᵧᵧ*", "m₂₂*"])
floatfmt.extend([".3f"] * 3)
table = tabulate(
mass_info,
headers=headers,
numalign="right",
stralign="center",
floatfmt=floatfmt,
)
click.echo("\nEffective masses:\n")
click.echo(table)
return mass_info